text:
|Publication number
|Publication type
|Application number
|Publication date
|Dec 7, 1993
|Filing date
|Jun 29, 1992
|Priority date
|Jun 29, 1992
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|Ben K. Jang, Ralph Schaetzing
|Original Assignee
|Eastman Kodak Company
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|Patent Citations (16), Non-Patent Citations (4), Referenced by (149), Classifications (15), Legal Events (9)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates in general to digital image processing, and, more particularly, to a method for automatically separating (segmenting) the desired regions in a digital radiographic image (i.e., the body part being imaged) from the undesired regions (areas around the body part that have received unattenuated radiation or have received very little radiation due to the use of beam limiting devices).
In medical imaging, accurate diagnosis of disease often depends on the detection of small, low-contrast details within the image. The probability of detection of these details, in turn, depends critically on their visibility in the image. A number of factors, including the body part being imaged, the exposure geometry and technique, and the characteristics of the detector can affect this visibility. In traditional screen/film radiography, for example, the characteristic curve of the screen/film system largely determines the contrast with which details are displayed in the final image. In digital radiography, on the other hand, the separation of image acquisition and display stages allows an image or portion of an image to be displayed at an arbitrary output contrast depending on the particular need. This is done by creating a look-up table (LUT) that maps the digital values of the acquired image into a new set of digital values that will be sent to the display device and written on some output medium (e.g., on film or on a CRT). In addition, other kinds of image processing are often used to try to enhance even more the visibility of various structures in medical images. For example, edge enhancement techniques can be used to increase the visual contrast of edges, and therefore the conspicuity of certain kinds of structures in an image.
The effectiveness of such image processing techniques depends on the careful choice of the various parameters that control their performance. Furthermore, the variety of exam types and diagnostic needs encountered in medical imaging generally requires that such parameters be chosen based on the image to be processed, rather than based on some set of fixed characteristics applicable to every image. For example, histogram-based tone-scale transformation is a simple and effective way of adjusting the contrast of an image. However, the histogram is a global characteristic of the image, and therefore does not distinguish between the desired or important regions of the image (e.g., the body part or portion being imaged) and the undesired or unimportant regions of the image (e.g., regions of direct x-ray background or areas of very low exposure behind collimator blades used to restrict the size of the irradiation field). Thus, a tone-scale transformation based on such a histogram will be suboptimum if it is unduly influenced by the unimportant regions of the image.
Even in image processing techniques that are locally adaptive within the image, i.e., that depend on the local context of the image, such unimportant regions can create unwanted effects. For example, in adaptive edge enhancement, some parameters of the algorithm often depend on the global histogram or on a local histogram (Sezan et al., IEEE Trans. Med. Imaging, vol. 8, p. 154, 1989). If this histogram contains data from the undesired regions of the image, the algorithm parameters will be negatively influenced and a suboptimum edge enhancement performance can be expected.
Thus, it is desirable to provide a method to detect and distinguish the undesired and desired regions of a digital radiograph. Such a method would allow the parameters for various image processing techniques to be calculated more easily. In addition, the values of these parameters would be close to the optimum values, leading to improved quality and better depiction of the information needed for an accurate diagnosis.
A variety of methods have been proposed to detect undesired regions in images. In the descriptions that follow, the word "background" will be used to denote the direct x-ray background, that is, the region around the body part being imaged that has received unattenuated x-rays. The word "foreground" will be used to denote highly (x-ray) absorbing structures, like collimator blades, used to define the size and shape of the irradiation field. Thus, a digital radiograph consists of three regions: the body part being imaged (the object), the background, and the foreground.
Histogram-based methods for detecting foreground and/or background usually depend on the detection and recognition of peaks in the histogram (see Sezan et al., IEEE Trans. Med. Imaging, vol. 8, p. 154, 1989). For example, U.S. Pat. No. 4,731,863, issued Mar. 15, 1988, inventors Sezan et al., teaches a technique for finding gray level thresholds between anatomical structures and image regions based on zero-crossings of a peak detection function derived from application of smoothing and differencing operators to the image histogram. This method produces a series of peaks by analyzing the histogram at several different signal resolutions. One problem with this method is that the peaks need to be interpreted for each exam type and exposure technique. That is, a low-signal peak could correspond to foreground, but it could also correspond to a body portion if no foreground is present in the image. Correspondingly, a high-signal peak may be the image background, or it may be a high-signal body portion. Thus, some additional information may be needed to make the method robust.
Other methods of histogram analysis have also been proposed. U.S. Pat. No. 4,952,805, issued Aug. 28, 1990, inventor Tanaka, teaches a foreground finding technique based on dividing the histogram into several sections with an intensity thresholding procedure and then doing a statistical shape analysis (discriminate analysis) of the section believed to correspond to foreground. A decision about the presence and extent of a foreground region is made based on the shape of this section of the histogram. However, as above, the large variety of histogram shapes that can occur with different exam types and different input modalities (such as magnetic resononance imaging (MRI), computed tomography (CT), ultrasound (US), nuclear medicine, digital subtraction angiography (DSA), computed radiography (CR)) make this type-of analysis subject to error. In addition, since a single threshold is chosen to represent the transition from foreground to object, this method does not perform well when the transition is fairly wide, such as when x-ray scatter is present.
European Patent Application 288,042, published Oct. 26, 1988, inventors Tanaka et al., proposes a foreground and background finding method using the image histogram. In this method, the histogram is again divided into a number of sections by an automatic thresholding procedure. Then a statistical analysis (discriminate analysis), combined with information about the exam type, exposure technique, and desired body portion to be displayed, is used to adjust the separation points between the sections until desired ranges for the foreground, object, and background regions are found. This method is less prone to variations in exam type and input modality because this information is incorporated into the decision process. However, the use of fixed thresholds still poses problems if there is nonuniformity in either the foreground or background.
Generally, histogram-based methods work best when the peaks in the histogram corresponding to foreground and background are far enough away from the peak(s) corresponding to the body part of interest that they can be identified as separate. If the peaks overlap, which can occur if the background or foreground are nonuniform across the image, or if scatter behind the collimators causes the signal level in that area to come close to the highly x-ray absorbing structures in the body, then it becomes more difficult to separate foreground/background regions in the histogram with a single threshold.
A more effective way of detecting foreground and background is to include spatial information about the image in the analysis, in addition to the intensity information provided by the histogram. Several methods have been described for doing this. U.S. Pat. No. 4,804,842, issued Feb. 14, 1989 inventor Nakajima, and U.S. Pat. No. 5,028,782, issued Jul. 2, 1991 inventor Nakajima, for example, disclose a method for detecting foreground in an image based on calculating derivatives of the input image and then identifying edge points as those points where the value of the derivative is higher than a threshold value. Then a new histogram of the input image is done using only the points identified as edge points, and from this histogram a threshold value is chosen to represent the boundary of the foreground (i.e., the irradiation field). This method is claimed to provide a more accurate measure of the field than a simple histogram method. However, it still requires an a priori knowledge that a collimator or field stop was in fact used to define the irradiation field, otherwise low-signal portions of the image inside the body part may be clipped by the intensity thresholding that defines the boundary. Furthermore, if image pixels inside the body part have a signal value comparable to or lower than those underneath the collimator region (as when there is significant x-ray scatter), the edge of the irradiation field may not even be found with this method. Finally, if the region under the collimator is nonuniform in intensity, which is frequently the case when there is scatter present, there will not be a strong edge at the boundary of the irradiation field, and the derivative at the edge points may not have a high enough value to pass the threshold, leading to inaccuracies in finding the edge.
Other foreground detection methods have been described that use one dimensional edge detection along arbitrary lines drawn across the image. For example, U.S. Pat. No. 4,967,079, issued Oct. 30, 1990, inventor Shimura, discloses a method for storage phosphor digital radiography systems that uses derivatives along radial lines from the image center, followed by a thresholding operation to detect potential edge points of the irradiation field. The boundary of the field is recognized by testing the colinearity of the found edge points. In order to be effective, this method requires a strong edge transition from object to foreground or from background to foreground. While the latter is generally true, the object to foreground transition can sometimes be very weak and even inverted (body part with a lower signal than foreground, due to scatter). Furthermore, if the image involves multiple smaller images recorded on a large detector (so-called subdivision or multiple exposure recording), there will be many edges detected along radial lines from the image center, possibly leading to the detection of false boundaries.
An alternate approach to foreground detection has been disclosed in U.S. Pat. No. 4,859,850, issued Aug. 22, 1989, inventor Funahashi. In this case, lines are extended from the edge of the image towards the center and, for each line, the transition regions from low signal to high signal at the edge of an irradiation field are fit with a linear or nonlinear equation. When the differences between the extrapolated fitted values (calculated from the equation) and the actual image values inside the field become too large, or when the extrapolated values reach a threshold signal level, the edge of the field is assumed to have been found. One problem with this method is that it assumes that collimation has been used (i.e., a priori knowledge of the exposure technique is required). A second problem has already been mentioned above, namely, that the method assumes that the signal values inside the irradiation field are always larger than those immediately outside it, which is not always the case when scatter is present. A third problem is that if subdivision recording has been used, the method may not find all of the necessary edges to define each irradiation field within the image. Finally, the use of multiple linear or nonlinear fits on multiple lines across the image is an inefficient, time-consuming way to find the field boundaries.
A possible solution to the previously mentioned problem of detecting edges in subdivision recording has been proposed in U.S. Pat. No. 4,851,678, issued Jul. 25, 1989, inventors Adachi et al. In this method, designed for storage phosphor digital radiography, potential edge points can still be found using the above method of differentiation along lines, but other possibilities are also disclosed. For example, once a few candidate edge points have been found, a boundary tracking procedure, based on following the likeliest edge points around the boundary from nearest neighbor to nearest neighbor (using a ridge-following algorithm) until they close on themselves again, is used to find the irradiation field. This method claims to handle multiple exposure fields as well since multiple starting edge points can each be followed around their respective irradiation fields. A third method using statistical template matching with a series of stored, commonly used subdivision patterns is also disclosed. Since the method of finding prospective edge points is similar to those above, similar potential problems exist, namely, that the method can break down when the edge transition from object to foreground is weak or inverted. Also the ridge-following algorithm can be very sensitive to noise, so the image data must be smoothed before the analysis.
As indicated above, the presence of multiple smaller images recorded on a single larger recording medium (i.e., subdivision recording) can create problems in locating all of the foreground in the image. Sometimes a preprocessing stage can be used to identify the use of subdivision recording and also the format of the image (2-on-1, 4-on-1, etc.). For example, U.S. Pat. No. 4,829,181, issued May 9, 1989, inventor Shimura, teaches a method of recognizing a subdivision pattern in a storage phosphor system using differentiation to detect prospective edge points, followed by a colinearity test to see if the edge points lie on straight lines. If the edge points lie on straight lines, subdivision recording is judged to be present. A limitation of this method is that it can detect only rectilinear patterns, i.e., patterns with essentially horizontal or vertical linear separations.
Another approach to detecting such subdivision patterns is the use of pattern matching. U.S. Pat. No. 4,962,539, issued Oct. 9, 1990, inventor Takeo et al., discloses a method that uses a set of binary, stored masks representing typical subdivision recording patterns. The input digital image is converted into a binary image by thresholding, and the resulting binary image is statistically compared With each of the masks in the stored set. The stored mask with the highest degree of matching is judged to be the recording pattern on the input image. While this method can handle a wider variety of patterns than the one above, it is still limited to the stored library of patterns for matching. Any irregular patterns not included in the library may not have a high degree of matching, and may therefore be chosen incorrectly. Furthermore, the statistical matching can be complex and time consuming.
According to the present invention, there is provided an automatic digital image processing method for detecting and distinguishing in digital radiographic images both the regions of direct x-ray exposure around the body part being imaged ("background") and the regions in which beam-limiting devices have been used to restrict the size of irradiation field ("foreground"). Thus, their adverse effects on data derived or calculated from said digital radiographic image can be eliminated, and the parameters of the image processing algorithms used to further process said image can be calculated easily and can be set to their optimum values, leading to improved diagnostic utility and high image quality.
For any digital radiographic input image, the foreground and background detection method according to an aspect of the present invention consists of five steps: edge detection, block generation, block classification, block refinement, and bit map generation. According to the first step of the method, the edge content of the image is analyzed. The local edge content is one indicator of the level of detail or "busyness" in various parts of the image, and, therefore, an indicator of whether these parts are more likely to belong to the foreground/background region or to the object. After the edge detection step, in the second step the image is broken into a set of nonoverlapping, contiguous blocks of pixels. Further analysis of the image and the classification into foreground, background, and object take place on a block-by-block basis.
The third method step is block classification. There are ten possible states each block can have, depending on whether the block is basically homogeneous (relatively low edge density) or nonhomogeneous (relatively high edge density), and on what it contains (foreground pixels, background pixels, object pixels, mixtures of foreground pixels with background pixels, or mixtures of foreground and/or background pixels with object pixels). The information about edge content along with similar information from neighboring blocks is fed into a syntactic reasoning section that uses a set of clinically and empirically determined decision rules to classify each block of the image into one of two categories: foreground/background or object. For blocks that are around the periphery of the object (i.e., mixtures of object pixels with foreground and/or background pixels), the next step of the method, a refinement step, evaluates the fine structure inside each block and reclassifies portions of the block into the above-mentioned two categories. This ensures that the edge pixels of the body part being imaged are not disclassified.
In the last step of the method, the block classification arrived at in the previous step is then used to create a two-valued (binary) image that functions as a template for any further image processing to be done on the image. For example, if a histogram of the image is required, only those pixels with the correct value in the binary image are included in the calculation. If edge enhancement is being performed, pixels in the foreground/background blocks are not included in the calculation of enhancement parameters. In this way, only the relevant information in the image is included in subsequent image processing of the image, leading to images with high quality and high diagnostic utility.
Another object of the present invention is to detect a plurality of irradiation subregions on a single image. The detection method consists of five steps: edge detection, block generation, block classification, region growing, and bit map generation. The first two stages are the same as those of the aforementioned foreground/background detection method. However, in the block classification step, a block is simply categorized into two states, depending on whether the block is homogeneous (relatively low edge density) or nonhomogeneous (relatively high edge density). A region-growing process, starting from the homogeneous corner blocks of the image, is then used to detect the plurality of irradiation subregions. Finally, a two-valued (binary) image is generated to function as a template such that the aforementioned foreground/background detection method can be applied to each irradiation subregion.
FIG. 1a is a block diagram of an imaging system in which the method of the present invention can be practiced.
FIG. 1b is a block diagram of an imaging system with the method of the present invention in place.
FIG. 1c is a block diagram of the method of the present invention.
FIG. 2a is a diagrammatic view of a chest image containing foreground, background and object regions.
FIG. 2b is the histogram of the image in FIG. 2a.
FIG. 3a is a diagrammatic view of a hand image containing foreground, background and object regions.
FIG. 3b shows the histogram of the image in FIG. 3a.
FIGS. 4a-g show the steps of the method of the present invention applied to the chest image of FIG. 2A.
FIGS. 5a-g show the steps of the method of the present invention applied to the hand image of FIG. 3A.
FIG. 6a is a schematic diagram showing the process of refining the nonhomogeneous (mixed) boundary blocks according to the method of the present invention.
FIG. 6b shows the histogram of the 8 neighboring blocks around the block [p,q] shown in FIG. 6a, used to compute the high and low threshold values.
FIGS. 7a-m show another mode of the present invention, the detection of a plurality of irradiation subregions on a single image.
FIG. 1a illustrates a typical system 10 in which the present invention could be practiced. The system consists of an image acquisition section (scanner) 12 in which the digital radiographic image signals 14 are obtained, a digital image signal processing section 16 in which the digital image signal data are manipulated with image processing algorithms intended to improve the appearance, diagnostic utility, and other properties of the image, and an image display section 18 in which the final processed output image signal 20 is made viewable. The method of the present invention would be inserted as a digital image segmentation section 22 into this imaging chain between the acquisition section 12 and the image processing section 16, as shown in FIG. 1b. The actual implementation of the present invention does not need to be separated from these two sections. It could, of course, reside directly in the acquisition section 12 or in the image processing section 16. In this way, the separation or segmentation of the image signal 14 into relevant and irrelevant or desired and undesired regions will occur before any other image processing takes place.
The digital radiographic image signals to be processed with this invention can be acquired in a variety of different systems, for example, from storage phosphor (computed radiography) systems, film digitizer systems, image intensifier systems, etc. In addition, one can use images acquired on computed tomography (CT) and magnetic resonance imaging (MRI) systems. For display devices, the most common are film writers (either laser printers or cathode ray tube (CRT) printers) and CRT soft displays.
In general, as shown in FIG. 1c, the digital image segmentation method of the present invention includes the following steps, edge detection 24, block generation 26, block classification 28, block refinement 30 and bit map generation 32. For purposes of illustration, the operation of the present invention will be described with reference to a digital chest image signal, as shown in FIG. 2a. FIG. 2a shows a chest image signal containing foreground 34, background 36, and object regions 38 as defined in the present invention. The object region 38, in this case is the region inside the body part being imaged, that is, the region bounded on the left and right of the object by the skin line, and on the top and bottom of the object by the edge of a beam limiting device (collimator). The histogram of this digital image signal is shown in FIG. 2b as a graphical representation of the number of pixels at each pixel intensity value.
The first step in the method of the present invention is to detect and characterize the edges in the image signal, and, in particular, to find the density of edges in various parts of the image signal. In order to determine the edge density, an edge detection step is used. A variety of edge detection algorithms are known in the literature (see, for example, A. Rosenfeld, A. C. Kak, Digital Picture Processing, Academic Press, 1982), many of which may be suitable for use as edge detectors in the present invention. In the preferred embodiment of this invention, a morphological edge detector has been chosen because of its low sensitivity to noise, and its ability to yield an edge strength measure that is proportional to the contrast between the foreground/background and the object and is independent of the edge position and orientation. A brief discussion of morphological techniques is given in Appendix A.
It is preferable to operate the edge detection step on a smoothed version of the input image signal, rather than the original image signal. This is because the edge detection step can be sensitive to noise and may therefore create edge pixels in regions of the image where there really are no edges. In order to create the smoothed version of the input image, a spatial filtering operation is used. For example, a local average of the eight nearest neighbors could be used to produce the smoothed image signal, although other methods are available. The expression for the edge signal produced with the morphological edge detector of the preferred embodiment of this invention is given by:
g(x,y)=min[ƒoB-ƒθB,ƒ⊕B-ƒ.multidot.B](x,y) (1)
where ƒ is the smoothed input image, and B is the structuring element of the morphological operation.
The size and shape of the structuring element must be matched to the size of the image and the structures the edge detector is intended to find. Smaller structuring elements are capable of locating smaller features and are more computationally efficient than larger structuring elements, which are sensitive to coarser structures in the image. Thus, if the desired structures are reasonably large (i.e., containing relatively many pixels) in the smoothed input image signal, ƒ, it is often easier and more efficient to scale down the input image signal by decimation (subsampling) or averaging than to scale up the structuring element to match the size of the details of interest. Furthermore, if the structuring element can be made separable, that is, the two-dimensional structure B(x,y) can be represented as a product of two one-dimensional windows, then the computational effort of the edge detection stage can be reduced considerably, and the one-dimensional images produced by the separable structuring element, denoted by gx and gy, can be recombined into a single edge signal image, for example, by quadrature: ##EQU1##
The output of the edge detection step is an edge strength map signal, g(x,y), containing high-amplitude pixels in areas of edges and low-amplitude pixels in areas of weak or no edge. This map image signal must be thresholded in order to include only the most significant edges needed to distinguish object and foreground/background regions in the image signal. The choice of this (global) threshold can depend on factors like the exam type, the typical noise content of the input modality, and the amount of smoothing used in the preprocessing stage. Pixels in the edge strength image, g(x,y), with values greater than the threshold Te are considered to be edge pixels. The remaining pixels are considered to be non-edge pixels. In other words, a binary edge image, E(x,y), is produced from the edge strength map by thresholding: ##EQU2## This is shown in FIG. 4c.
In order to make the thresholding step more robust, the edge strength map, g(x,y) can be compared to several thresholds calculated in different ways. For example, the threshold can be considered to be proportional to the maximum value of g(x,y):
Te =α1 �max[g(x,y)] (4)
In this way, the threshold is adjusted to match the actual dynamic range of the edge signals. The proportionality constant, α1, must be determined experimentally for a variety of exam types. Alternatively, a threshold can be found by requiring that some fixed proportion, α2, of the detected edge pixels be retained in the edge strength image. Here again, the proportionality constant, α2, is empirically determined based on characteristics of the image class. Other methods of threshold selection are also possible. In the preferred embodiment of the present invention, the method is to automatically choose a threshold that maximizes the sum of the gradient values, g(x,y), taken over all pixels with a value equal to the threshold: ##EQU3## Since this method essentially involves the product of a threshold value and the number of pixels at that value, it is especially useful in cases when there are strong edges involving many pixels, as in the case of collimator blade edges delimiting an irradiation field.
The next step of the preferred method of the invention is to divide the image signal into a set of nonoverlapping, contiguous blocks of pixels for further analysis. The size of the pixel blocks depends on the size of the image. The blocks should be large enough to be able to calculate local statistical properties (e.g., mean and standard deviation), but small enough to resolve the structures necessary for accurately segmenting the foreground and background. By experimentation, the inventors have found that block dimensions on the order of 1/20th of the linear dimension of the input digital image are ideal for this division, but block sizes from 1/10th to 1/30th of the linear dimension of the input image all give similar results.
Once the image has been divided into pixel blocks, the next step according to the invention is the characterization or classification of each block. Each block can have ten possible states, depending on its degree of homogeneity and what it contains. The ten possible states of each block are:
(a) homogeneous background
(b) homogeneous foreground
(c) homogeneous object
(d) nonhomogeneous background
(e) nonhomogeneous foreground
(f) nonhomogeneous object
(g) mixed: background+foreground
(h) mixed: background+object
(i) mixed: foreground+object
(j) mixed: background+foreground+object
The precise definitions of homogeneous and nonhomogeneous will be given below. A feature of the present invention in the context of the above list is to find all of the blocks in the image containing object pixels and to separate them from the blocks containing only foreground/background pixels. The blocks that exhibit mixed characteristics, i.e., that contain object pixels mixed with foreground and/or background pixels, are further classified in a refinement stage that extracts only the object region of each block.
Once the thresholded edge strength map, E(x,y) has been found according to Eq. (3), and the image signal divided into a set of pixel blocks, the next step is to classify the blocks. The first classification is into homogeneous and nonhomogeneous blocks. A block is defined as homogeneous if it has no or very few edge pixels, and has a low variance. Any block that does not meet these two criteria is considered nonhomogeneous.
The edge density, md, for a block can be defined as the ratio of the number of edge pixels in a block (after thresholding) to the total number of pixels in the block. If the image size is Nx pixels/line and Ny lines, and the block size is Sx by Sy, then the edge density for BLOCK(p,q) is given by: ##EQU4##
The variance of BLOCK(p,q) can be calculated by standard statistical methods as: ##EQU5## where l and r are the left-most and right-most nonzero gray levels in the local histogram h of BLOCK(p,q) and μ is the mean gray level of the block.
In the preferred embodiment of this invention, each block is subjected to the aforesaid two tests for edge density and variance. These tests involve a threshold comparison for each block. The edge density threshold for each block, Td, can, for example, be chosen as some fixed fraction of the edge density of the entire image, that is, ##EQU6## where β is the fixed fraction, which can be determined empirically. Similarly, the threshold for the variance, Tv, can be determined by experiments.
Using the combined results of these two tests, it is possible to make an initial classification of each block in the image. Specifically, one can create a binary block map H[p,q] that classifies each block as homogeneous (H=0) or nonhomogeneous (H=1): ##EQU7## This is shown in FIG. 4d. The blocks that have been classified as homogeneous can be homogeneous foreground or background as well as homogeneous object regions (e.g., the subdiaphragmatic region in a chest image can sometimes be homogeneous). Further tests will be required to distinguish the homogeneous object regions from the homogeneous foreground/background regions. In addition, the blocks classified as nonhomogeneous can consist of nonhomogeneous foreground, background, and/or object, as well as combinations of the three. In order to further classify these regions two other steps of analysis, syntactic reasoning and refinement, are used.
The methods proposed in the prior art described above generally use low-level image processing techniques similar to those described so far for the present invention, e.g., the detection of edges, the characteristics of local or global histograms, and global thresholding. In order to provide a more accurate segmentation of the various regions in a digital radiograph, and thereby provide images of higher diagnostic utility, it is preferable that higher level image processing techniques be used to refine and modify the results obtained with the lower level techniques. In the current embodiment of the present invention, a high-level processing technique called syntactic reasoning (see K. S. Fu, Syntactic Pattern Recognition, Academic Press, New York, 1974) is first used to analyze and improve the results of the low-level stage.
In syntactic pattern recognition, a set of rules (grammar) is used to define the relationships between the various anatomical structures in different classes of images. The inventors have found by experimenting with a variety of exam types and input modalities that the rules disclosed below give an accurate and efficient segmentation of a large variety of digital radiographic images.
A number of notations are introduced here to abbreviate the descriptions of the following rules. For every nonhomogeneous (homogeneous) block H[p,q]=1 (0), let Cpq be the connected component consisting of all nonhomogeneous (homogeneous) blocks connecting to H[p,q]. The number of blocks contained in Cpq is defined as the size of Cpg and denoted by SIZE(Cpg). The total number of blocks in the image is given by PQ=P�Q, where P and Q have been defined in Eq. (7). The distance between two connected components Cpg and Cp'q' is denoted by DIST(Cpq, Cp'q'). Finally, the average gray level of the block [p,q], denoted as μ[p,q], is given by ##EQU8## Finally, the output of syntactic reasoning is represented by the binary block map S[p,q], which has the value of 0 if the block [p,q] belongs to nonobject regions and the value of 1 if otherwise.
Rule 1: IF [H[p,q]=1] and [SIZE(Cpq ≦γ1 �PQ] and DIST(Cpq, The image boundary)<γ2 �PQ], THEN S[p,q]=0.
This rule is designed to detect isolated nonhomogeneous foreground/background regions, which may be caused by, for example, noise, nonuniform illumination, and labels (e.g., patient identification) attached to the image. The parameters γ1 and γ2, depend on the exam type and can be determined empirically.
Rule 2: IF [H[p,q] and [Rule 1 is not true], THEN S[p,q]=1.
This rule is designed to detect nonhomogeneous object regions.
Rule 3: IF [H[p,q]=0] and {[(SIZE(Cpq)≧γ3 �PQ) and (DIST(Cpq, The image boundary)≧γ4 �PQ)] or [TF ≦μ[p,q]≦TB ]}, THEN S[p,q]=1.
This rule is designed to detect isolated homogeneous object regions (e.g. the subdiaphragmatic region in a chest image can sometimes be homogeneous. The parameters γ3 and γ4, depend on the exam type and can be determined empirically. The determination of the thresholds TF and TB will be described below.
Rule 4: IF [H[p,q]=0] and [Rule 3 is not true], THEN S[p,q]=0.
This rule is designed to detect homogeneous foreground/background regions.
The resulting image S[p,q] is shown in FIG. 4e.
The computation of the foreground and background thresholds, TF and TB, is done as follows. First, a histogram of the gray-level values of only the edge pixels is computed (the edge pixels are the pixels equal to 1 in the image E(x,y)). The minimum and maximum values of this histogram, λmin and λmax, are used to calculate the thresholds:
FF =λmin +γ5 (λmax -λmin) (12)
TB =λmax -γ5 (λmax -λmin) (13)
where γ5 is a parameter determined experimentally from a large variety of clinical images.
At this point the majority of the blocks in the resulting image S[p,q] have been classified as object (i.e., S[p,q]=1) or nonobject (i.e., foreground/background, S[p,q]=0). However, some blocks containing the boundary of the object may still consist of a mixture of object and foreground or background. In order to assign the appropriate thresholds for these blocks, a refinement step is used to analyze them further. Let the output refined image be denoted by F(x,y) with the value 1 indicating the body part and 0 the foreground/background. The refinement process is an adaptive thresholding process that iteratively removes from each boundary block ∂S[p,q] those pixels with gray levels greater than a threshold TH (p,q) (i.e., background) or less than a threshold TL (p,q) (i.e., foreground). This can be formulated as a rule:
Rule 5: IF [((x,y)⊖∂S[p,q]) and (TL (p,q)≦ƒ(x,y)≦TH (p,q))] or [(x,y) ∂S[p,q]], THEN F(x,y)=1.
Rule 6: IF [Rule 5 is not true], THEN F(x,y)=0.
Consequently, the bit map (binary image) F(x,y) is created, and this segmentation result is used as a template for any further image processing to be done on the image. This is shown in FIG. 4f. The histogram of the gray level data from the segmented image (the body part) is shown in FIG. 4g.
The determination of the thresholds TL (p,q) and TH (p,q) for each block of interest can be done, for example, by looking at the properties of neighboring blocks. If μ.sub.ƒ (p,q), σ.sub.ƒ (p,q), denote the mean and standard deviation of the blocks identified as foreground among the eight neighboring blocks around a block of interest, and μb (p,q), and σb (p,q) denote the mean and standard deviation of the blocks identified as background among the eight neighboring blocks around the same block, then the thresholds are computed by:
TL (p,q)=μ.sub.ƒ (p,q)+δσ.sub.ƒ (p,q)(14)
TH (p,q)=μb (p,q)-δσb (p,q) (15)
The parameter δ can be determined empirically. If an object boundary block also happens to be on the boundary of the image, at least two neighboring blocks will be available to compute the thresholds. FIG. 6a shows a diagram for the process of refining the nonhomogeneous (mixed) boundary blocks. FIG. 6b shows the histogram of the block [p,q], as well as the high and low thresholds computed from the 8-neighboring blocks of the block [p,q]. Those pixels in the block [p,q] with gray levels greater than TL (p,q) and less than TH (p,q) are considered to be object pixels.
The results of extensive experimentation by the inventors on clinical images from a variety of sources have confirmed the ability of the present invention to determine accurately the foreground, background, and object regions in a digital radiograph. Examples of the various stages of the method applied to an extremity (hand) image are shown in FIG. 5a through 5g.
The parameters of the method, which need to be determined through experimentation are given in Table 1 for four different exam types, chest, extremity, skull, and abdomen. These were determined by using the method on images acquired from storage phosphor systems and scanned film systems. It has been observed by the inventors that the typical values for β, Tv, γ5, and δ are 0.1, 8.5, 0.25, and 1.0, respectively. The other parameters that are employed in the syntactic reasoning depend on the exam type. In the case of the storage phosphor images, the original images were acquired at a resolution of 2048 pixels/line and 2500 lines and 12 bits/pixel. In order to make the foreground/background finding method of the present invention more efficient, the image was subsampled by a factor of 9 in both directions, resulting in an image 227 pixels by 277 pixels in size. The block size was selected to be 10 pixels by 10 pixels, which is about the order of 1/20th of the linear dimension of the subsampled image. A 3'3 structuring element, which can be represented as a product of two one-dimensional windows (i.e., 3�1 and 1�3 windows), was used in the aforementioned morphological edge detector. The method was applied to this image using a SUN 4/110 workstation with the algorithm implemented in software. The output of the method was a 1-bit image with 0 indicating the foreground/background regions and 1 indicating the object regions. This result was achieved in approximately 10 seconds. Hardware implementation is obviously also possible, with a significant reduction of processing time.
The image size of the subsampled image chosen for the preferred embodiment of the present invention was based on a series of tests with image sizes ranging from full resolution (2048�2500) to 64�78. There was very little influence on the quality of the result with image size until the smallest sizes were reached (subsampling factors greater than approximately 16). Thus, efficient operation can be achieved even though the present invention involves higher level syntactic analysis in order to do the segmentation accurately.
TABLE 1______________________________________Chest Extremity Skull Abdomen______________________________________β 0.1 0.1 0.1 0.1Tv 8.5 8.5 8.5 8.5γ1 0.50 0.15 0.15 0.50γ2 0.005 0.010 0.015 0.005γ3 0.1 0.25 0.25 0.1γ4 0.005 0.03 0.03 0.015γ5 0.25 0.25 0.25 0.25δ 1.0 1.0 1.0 1.0______________________________________
Another mode of the present invention is to detect a plurality of irradiation subregions on a single image. This is shown in FIG. 7a. The detection method consists of edge detection, block generation, block classification, region growing, and bit map generation. In the preferred embodiment of this invention, a morphological edge detector as described in the aforementioned foreground/background detection method is used. This is shown in FIG. 7b. An image is then divided into a set of nonoverlapping, contiguous blocks, and each block is classified as homogeneous or nonhomogeneous according to Eq. (10). The resulting image, denoted by H, is shown in FIG. 7c. To confine the following region-growing process, the complement of H, denoted by Hc, is created. This is shown in FIG. 7d.
The region-growing stage is an iterative process which begins by setting the homogeneous corner blocks as the initial seeds. This is shown in FIG. 7e. In each iteration, the seeds are expanded to their 4-neighbors (i.e., the right, left, top, and bottom blocks), and the intermediate result is intersected with Hc, yielding a new set of seeds to be grown in the next iteration. The process repeats until there is no change in any two successive iterations. FIGS. 7f through 7k show the seeds after 2, 3, 4, 5, 10, and 15 iterations, respectively. The converged result is shown in FIG. 7l where the seeds image does not change even if the iteration continues.
Thus, a bit map of a plurality of irradiation subregions can be obtained by complementing the converged seeds image, which is shown in FIG. 7m. The aforementioned foreground/background detection method can then be applied to each subregion of the image, leading to images with high quality and high diagnostic utility in each subregion.
Mathematical morphology is a set-theoretic method for image analysis. Its objective is to provide a quantitative description of the geometric structure of an image. The general approach is to probe and transform an image with different patterns called structuring elements.
The basic operations are erosion and dilation. Erosion can be viewed geometrically as a transformation that shrinks a set. For a binary image signal X, an erosion by a structuring element B is defined as the intersection of all translations of X by the points bεB, that is, ##EQU9## The dual operation of erosion is dilation, which is defined as the union of all translations of X by b, that is, ##EQU10## and can be viewed geometrically as a transformation that expands a set X. When a signal is eroded and then dilated, this operation is called an opening and is denoted by
Similarly, closing, denoted as
is defined by first dilating the signal and then eroding the output signal. The binary morphological operations of dilation, erosion, opening, and closing are all naturally extended to gray-level images by the use of minimum or maximum operation. The definition of erosion, dilation, opening and closing of a gray-level signal f by a binary structuring element B are listed as follows: ##EQU11## See Haralicok et al., IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, p. 532, 1987, for more details and references.
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|Jul 24, 2008
|Carestream Health Inc
|Roi-based rendering for diagnostic image consistency
|U.S. Classification
|382/132, 382/180
|International Classification
|Cooperative Classification
|European Classification
|Jun 29, 1992
Owner name: EASTMAN KODAK COMPANY, A CORP. OF NJ, NEW YORK
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST.;ASSIGNORS:JANG, BEN K.;SCHAETZING, RALPH;REEL/FRAME:006174/0950;SIGNING DATES FROM 19920601 TO 19920611
|May 22, 1997
|Fee payment
Year of fee payment: 4
|May 29, 2001
|Fee payment
Year of fee payment: 8
|Mar 29, 2005
|Fee payment
Year of fee payment: 12
|Jul 27, 2007
|Mar 7, 2008
Owner name: CARESTREAM HEALTH, INC.,NEW YORK
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:EASTMAN KODAK COMPANY;REEL/FRAME:020741/0126
Effective date: 20070501
Owner name: CARESTREAM HEALTH, INC.,NEW YORK
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:EASTMAN KODAK COMPANY;REEL/FRAME:020756/0500
Effective date: 20070501
|Apr 4, 2011
Owner name: CARESTREAM HEALTH, INC., NEW YORK
Free format text: RELEASE OF SECURITY INTEREST IN INTELLECTUAL PROPERTY (FIRST LIEN);ASSIGNOR:CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH;REEL/FRAME:026069/0012
Effective date: 20110225
|May 12, 2011
Owner name: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, NEW YORK
Free format text: INTELLECTUAL PROPERTY SECURITY AGREEMENT;ASSIGNORS:CARESTREAM HEALTH, INC.;CARESTREAM DENTAL, LLC;QUANTUM MEDICAL IMAGING, L.L.C.;AND OTHERS;REEL/FRAME:026269/0411
Effective date: 20110225
|Mar 13, 2012
Owner name: CARESTREAM HEALTH, INC., NEW YORK
Free format text: RELEASE OF SECURITY INTEREST IN INTELLECTUAL PROPERTY (SECOND LIEN);ASSIGNOR:CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH;REEL/FRAME:027851/0812
Effective date: 20110225


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Trudeau apologizes for anti-Alberta comments Add to ...
Featuring video from CTVNews.ca
Last updated
Political Analyst Alise Mills explains why she feels Justin Trudeau's anti-Alberta comments have somewhat discredited him.
Watch More Video:
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B.C. health officials are warning flu vaccine will under-perform
Shooting victims in stable condition at hospital
Kenney says the niqab represents a 'misogynistic view of women'
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Listen to the emergency call from the Oregon shooting
Vigil held for family devastated by Ontario crash
Opposition leaders outline focus for final election debate
Obama calls for gun reform after Oregon shooting


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Club Executives & Directors
Bulletin Subscribe
Club Information
Celebrating 90 Years of Service to Weyburn and International Communities
Service Above Self
Royal Canadian Legion
Weyburn, Saskatchewan S4H 0W2
District Site
Venue Map
Home Page Stories
Rotary has partnered with STARS for this event. Funds raised will be shared equally between Rotary and STARS. Rotary's share will help support many local community and international projects. Examples include the Weyburn Hospital Foundation, Camp Easter Seal, Care-A-Van Society, Habitat for Humanity, student educational scholarships, Polio-Plus, Shelter Box program and many other groups and organizations.
In conjunction with "The Drop", Rotary is hosting a Golf tournament as well as a 50/50 Putting Contest.
Tickets are available from Rotary Members.
Tickets sell for $5.00 or $50.00 per book.
A total of 6000 tickets are available for purchase. Don't miss your opportunity to support Rotary, STARS and win prize money
1st -$5,000.00
2nd - $3,000.00
3rd - $1,000.00
Mark your calendar for Friday August 7, 2015.
In conjunction with 2015 Golf Ball Drop, Rotary Club of Weyburn is hosting a Golf Tournament.
Individuals or teams of 4 may register at Weyburn Golf Club Pro Shop 306 - 842- 5881
Includes Golf, Par 3 Hole in One insurance, novelty holes, prizes by flight, fun, and barbeque supper following the Golf Ball Drop.
Test your skill (or Luck) at the 50/50 Putting Contest before the Tournament.
Registration is $125.00 per Player.
Proceeds shared with STARS and Rotary Club of Weyburn.
Take the summer afternoon to join Rotary and have some fun.


text:
New to ePHOTOzine? Join ePHOTOzine for free!
Upload photos, chat with photographers, win prizes and much more for free!
This blog shows you how I made crate paper flowers to photograph.
Making these crate paper flowers can be time consuming so if it's raining out side and your bored. this is a ideal project.
Not only can you make pretty flowers from scratch . you can also use them to practice your lighting and Macro techniques.
click the blue writing below to be taken to my photographic guide.
How to make Crate paper flowers
ePHOTOzine, the web's friendliest photography community.
Upload photos, chat with photographers, win prizes and much more.


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Chicago Public Media
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Subscribe to youth
Dear Chicago: Stop criminalizing our youth
Feb. 7, 2011
var so = new SWFObject("http://www.db798.com/pictobrowser.swf", "PictoBrowser", "500", "520", "8", "#EEEEEE"); so.addVariable("source", "sets"); so.addVariable("names", "Dear Chicago: Stop criminalizing our youth"); so.addVariable("userName", "chicagopublicmedia"); so.addVariable("userId", "3
Dear Chicago: Stop criminalizing our youth
Feb. 7, 2011
Allegations of police misconduct are not new to Chicago.
Distorting the truth about youth violence?
Jan. 3, 2011
Ever since I started writing about the killing of Jeremiah Sterling, my friend Brendan Shiller has been pushing back, telling me that my because I focus so much on the actual violence and loss, I'm contributing to the impression that violence is greater now, when in fact the murder rate in Chi
Suburban school district vows to defend expulsion
Nov. 4, 2010
A suburban school district says it'll fight a lawsuit aimed at overturning the expulsion of a student. Proviso Township High Schools says kicking him out was necessary for the safety of other students.
Revision Street: Voices from the Whittier Field House—Ociel, Ramón, Manuel, and Adrianna
Oct. 25, 2010
It's the 41st day of the occupation of the Whittier Dual Language Elementary School field house, also known as La Casita.
Revision Street: Voices from the Whittier Field House—Ociel Gomez (II)
Oct. 21, 2010
The occupation at Whittier Dual Language Elementary School Field House will soon be coming to a close—just as soon as CPS puts their verbal agreements with the Whittier parents in writing.
Revision Street: Voices from the Whittier Elementary School Field House -- Nancy Galan, 14
Oct. 18, 2010
It's been about a week and a half since we last visited Whittier Dual Language Elementary School, and much has changed since then. Certainly, even more has changed since they took over the field house 34 days ago. Thirty four! Over a month.
Louder Than a Bomb takes to the airwaves tomorrow
Jul. 3, 2008
When I first hosted the Louder Than a Bomb finalists here in 2005, I couldn't have been more impressed with these remarkable young writers. Never having heard slam poetry before, I was struck by their confidence (not so sure I could have done that when I was 15 years old).
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ACR Sandfire
this wiki
|Built off of the ACR, the ACR Sandfire features a unique camouflaged exterior that is highly useful in specialized combat operations.
|— The Arsenal
The ACR Sandfire is the NX Standard variant of the ACR that was released during the 4-27-10 Patch.
The ACR Sandfire comes with two more damage points, four more portability points, the same rate of fire, two more accuracy points, and one more recoil point, furthermore it comes with a desert, sandfire camouflage.
Statistically and gameplay wise, the ACR Sandfire is quite similar to both the M6A2-SRT and the MK16 SCAR-L, favoring more the style of the SCAR-L. Because of this overall similarity the popularity has remained rather low, with most NX users going with the M416 CQB, or a more popular weapon. Though one of its features is a quick reload, where the wielder inserts the magazine and is ready to fire.
|GP Standard
|NX Standard
|ACR Sandfire
|NX Rare
|ACR Blacksnake
- During the weekend of 9/10/10, you could get a free 1-Day ACR Sandfire if you played 20 Elimination matches.
- As of January 2011, Nexon has retired the weapon from being purchased permanently. It was being sold for 9,950 NX for permanent on Wednesday 01-05-11 till Wednesday 01-12-11 at 11:00 am PST.
- This gun was one of the rewards for the 2011 5-shot event.
- During the Memorial Day Celebration sale event the ACR Sandfire was for permanent duration for 17,430 NX.
- It was available for for 40% off permanent duration during the Shut Up Sale Encore.
- During the Return of the Perms Sale, it was available for 19,920 NX permanent duration.
- During the Black Friday Sale (2012), this was sold at 18,675 NX for permanent duration.
- The ACR Sandfire is the second Sandfire weapon, with the first being the SR25 Sandfire. Strangely, their camouflages are different, with the SR25 being just colored in desert color, while the ACR has a camo with several different colors.
- Despite having 1 point more recoil than the GP-bought ACR, the ACR Sandfire's recoil pattern is totally different from the ACR. The ACR Sandfire's recoil goes up for the first few shots, and then diagonal, whereas the ACR goes sideways like the SCAR-L.
- The iron sight of the ACR in game is flipped down.
- During the weekend of 9/10/10, you could get a free 1-Day ACR Sandfire if you played 20 Elimination matches.
- If you look at it, it doesn't look like sand, but more like a desert camo.
- It has a different firing sound from the GP standard ACR.


text:
Usually I only write my articles in German because my English is bad. But anyway – this is not a piece of literature but a small piece of the seo-puzzle "Google Phantom Update (2)". In short: From my point of view the mysterious Phantom-Update is an "user intention update". That means: no decreasing site has failed. All these sites are as well as before. They might be better than the first results, but … Google has changed that part of the algorithm that decides about the user intention. It's not an update of the site-specif ranking factors – it's about the quality of the Search engine result page itself. Read on »
SEO-stuff 1 Kommentar 11. Juni 2015


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All hotels in Hazelwood, Missouri, United States of America
1 room, 2 adults
318 Taylor Rd, Hazelwood, MO, 63042, United States of America, Hotels.com Expert:
La Quinta Inn St. Louis Hazelwood - Airport North - Hazelwood
Lowest priceRs4,325
Best Price Guarantee
No-frills hotel in Hazelwood
- Free continental breakfast, free WiFi, and free parking
Main amenities
- 109 guestrooms
- Fitness center
- Airport shuttle
- 24-hour front desk
- Air conditioning
- Daily housekeeping
- Laundry service
- Meeting room
- Multilingual staff
- Free newspapers in the lobby
- Lobby TV
What's around
- Lambert - St. Louis International Airport
- St. Louis Mills Mall (4.5 mi / 7.2 km)
- Hollywood Casino Amphitheatre (7.9 mi / 12.6 km)
- St. Louis Car Museum (9.2 mi / 14.8 km)
- St. Louis Art Museum (15.1 mi / 24.3 km)
- First Missouri State Capitol Historic Site (9.8 mi / 15.8 km)
La Quinta Inn St. Louis Hazelwood - Airport North offers an airport shuttle during limited hours. Guests can visit the fitness center for a workout, and free perks include continental breakfast, WiFi, and self parking. Rooms offer flat-screen TVs and free wired Internet.
Stayed overnight to go to Rams football game the next day. The room was clean and…
Stayed for a wedding. Great location and price. Beds not quite as comfy as others and not…
TripAdvisor Traveller Rating: 3.5
From 201 reviews
Enter dates to check prices
Hotel Info: from Rs4,325
- Room, 2 Double Beds, Refrigerator & Microwave
- Room, 1 King Bed
- Standard Room, 1 King Bed
- Standard Room, 2 Double Beds
Recommended hotels
At a glance
Key facts
Hotel size
- This hotel has 109 rooms
- This hotel is arranged over 4 floors
- Check-in time starts at 3 PM
- Check-out time is noon
- Express check-in/out
Required at check in
- Credit card or cash deposit required
- Government-issued photo ID required
Travelling with others
- Pets allowed
- Free WiFi in public areas
- Free WiFi and wired Internet access in rooms
- Free self parking
- Accessible parking
Nearby Two Star Hazelwood hotels
In the hotel
Food and drink
- Free continental breakfast
Things to do
- Fitness facilities
Working away
- Conference space size (feet) - 400
- Conference space size (meters) - 36
- One meeting room
- 24-hour front desk
- Dry cleaning/laundry service
- Laundry facilities
- Free newspapers in lobby
- Number of buildings/towers - 1
- Elevator/lift
- Television in lobby
- Disabled parking
- Accessible bathroom
In the room
Home comforts
- Air conditioning
- Coffee/tea maker
- Iron/ironing board
Freshen up
- Shower/tub combination
- Hair dryer
Be entertained
- Flat-screen TV
- Premium TV channels
Stay connected
- Desk
- Free WiFi
- Free local calls
- Daily housekeeping
Top Hazelwood hotels
Recently reviewed hotels in Hazelwood
- Days Inn St. Louis Westport MOFrom
"Had a very comfortable stay for a long period (22 days)... Staff was very friendly...The room was large and clean...At decent price it has all the necessary ameneties (swimming…"Jul 22, 2014
- Missouri Athletic ClubFrom
- Sheraton Clayton Plaza HotelFrom
"very friendly and helpful staff prompt answers and responses clean comfortable"Mar 25, 2013


text:
Free Shipping
Product Information:
The Winsome Kallie 3 Piece Pub Table Set will transform your dining area with its simple yet stylish design. This set includes one pub table and two swivel stools. It is available in multiple finishes that you can choose from as per your aesthetic sense. The pub table and stools have a metal and wood construction, which makes them durable and sturdy. The Kallie 3 Piece Pub Table Set by Winsome takes up minimum space and complements modern themed interiors. The height of these swivel stools can be adjusted to meet your requirements. It is CARB compliant and requires minor assembly.
Weights & Dimensions
|Product Type: Pub table set
|Table Base Type: Pedestal
|Table Shape: Round
|Style: Modern
|Distressed: No
|Table Base Material: Metal
Table Top Material: Wood
|Number of Items Included: 3
|Pieces Included: Table and 2 stools
Stools Included: Yes
|Table Casters Included: No
|Shelves Included: No
|Drawers Included: No
|Cabinets Included: No
|Custom Made: No
|Drop Leaf: No
|Extension Included: No
|Foldable Table: No
|Table Foot Rest Included: No
|Wrought Iron: No
|Adjustable Table Height: No
|Commercial Use: No
|Eco-Friendly: No
|Product Care: Wipe clean with a cloth
|Country of Manufacture: China
Assembly Required: Yes
Installation Required: Yes
|Additional Parts Required: No
|Product Warranty: Replacement parts within 60 days from date of purchase
|CARB Compliant: Yes
ANSI BIFMA Compliant: Yes
|CPSIA or CPSC Compliant: Yes
|FSC Certified: Yes
|ISTA 3A Certified: Yes
About the ManufacturerFor over three decades, Winsome Trading Inc. has been a manufacturer and distributor of high quality pantryware and lifestyle furniture for the home. Specializing in solid wood, Winsome has continually brought forth innovative products in combinations of solid wood and other various materials such as wrought iron, aluminum, steel, marble and glass.
More About This Product
Free Shipping
Ships to:
Ships via: UPS/FedEx
Enter your shipping zip or postal code to calculate shipping estimatesGet Delivery Estimate
We want you to love it. If you don't, you may return your purchase within 30 days of delivery. All you need to do is visit our Self-Service Return Center for instructions. Return credits are issued within 24 hours once the item is received and inspected. Please note that shipping charges are non-refundable. The cost of return shipping will be deducted from your refund. 30-Day Return Policy
Overall Rating
Based on 33 reviews
Customer PhotosAdd your own photo
You Get What You Pay For Stephen from Spring, TX on 08/16/2013 – Verified Buyer
This is decent-looking, cheap, casual-use furniture, but you definitely get what you pay for. Based on these awesome reviews, I bought for a kitchenlounge area of a small business. While this set LOOKS nice, it is definitely not very practical. It will not hold up with regular use. The barstools and table are wobbly and the wood finish is easily damaged. Assembly was incredibly easy, but almost TOO easy. The barstools and table don't actually screw into the bases. They literally just are SET inside and held in place by gravity. I imagine this contributes to the wobbliness. The seats themselves are pretty small and not very forgiving of various body types. They are also slick. These are not barstools for relaxing in. The base of the table needs to be heavier and also needs some kind of rubber feet. If your floor is at all uneven (e.g. grouted tile), the table will wobble more that it already would. And finally, warning stickers on assembly warned that these are NOT for commercial use and that each barstool has a weight limit of 200 lbs. Wish AllModern had included that information in the product description.
Was this helpful? Yes 4 No 0
Great pub table! Jane from San Jose, CA on 05/27/2015 – Verified Buyer
This was super easy to assemble, no tools necessary and it looks great, is comfortable to sit on and I really like the height adjustment.
Was this helpful? Yes 0 No 0
Nice Set for the money David from Rochester Hills, MI on 03/04/2015 – Verified Buyer
Nice Set for the money, easy and fast to assemble. Not as sturdy as I would have hoped but it does the job
Was this helpful? Yes 0 No 0
Great style and easy to assemble Sheri from Green lane, PA on 12/02/2014 – Verified Buyer
Looks great and the color is as it appears in the pictures. It is not super sturdy but i didn't expect that for the price and reviews.
Was this helpful? Yes 0 No 0
OK so far! J. from MA on 10/08/2014 – Verified Buyer
We've had no problems as far as I can tell with our purchases from here! I am the credit card for my wife and she spends most her shopping here :) The pub table set ended up working out great for us!
Was this helpful? Yes 0 No 0


text:
Sen. Tom Coburn, a fierce critic of wasteful government spending, announced plans to retire when the Senate's current session ends in early 2015 -- two years before the end of his term.
As ranking member on the Senate Homeland Security and Governmental Affairs Committee, the Oklahoma Republican fought to reduce spending on duplicative government programs, and in recent years worked on legislation to eliminate improper payments. He is perhaps best known for releasing his annual Waste Book, a list of programs and expenditures that he felt squandered taxpayer money. Coburn targeted both outdated IT systems and agency conference and travel spending in recent years.
"No one has done more to awaken Americans to the threat posed by a government that chronically spends more than it takes in, and no one has worked harder at finding a solution," Senate Minority Leader Mitch McConnell (R-Ky.) said.
Coburn is not opposed to all spending, however. Though he has said he believes as much as half of the federal government's annual IT expenditure of $82 billion is wasted,in January he suggested that salaries for the federal IT procurement workforce could be raised to better compete with the private sector for talent.
An obstetrician by training, Coburn first came to Washington as part of the House Republican wave in 1994. He retired after three terms, then ran for the Senate in 2004. Coburn recently revealed he is in treatment for prostate cancer, but said that his illness is not behind his decision to leave the Senate.
"As a citizen, I am now convinced that I can best serve my own children and grandchildren by shifting my focus elsewhere. In the meantime, I look forward to finishing this year strong," Coburn said in a statement on his website.
It's not clear who will succeed Coburn as ranking member on the Homeland Security panel. John McCain (R-Ariz.) is next in seniority, but he's rarely seen at hearings. Ron Johnson (R-Wis.) and Rob Portman (R-Ohio) are more active in the government oversight activities of the committee, and one of them might be a more likely candidate.
Coburn joins a growing group of lawmakers announcing their departures. Reps. Buck McKeon (R-Calif.), chairman of the House Armed Services Committee, Jim Moran (D-Va.), whose district is home to a concentration of federal workers, and George Miller (D-Calif.), who has served 20 terms, are among those who have announced retirements this week.
About the Author
Adam Mazmanian is FCW's executive editor. Connect with him on Twitter: @thisismaz.
E-Mail this page
Printable Format
Advanced Search
Meet 100 women and men who are doing great things in federal IT.
Click here for the full list
The Census Bureau announced plans to fold in new technology and third party data into its 2020 population count, but questions remain about the viability of the Bureau's enterprise IT.
A technology roadmap can help agencies reap the benefits FITARA has to offer. Here are the four stages of a successful effort.
In the newest Federal Employee Viewpoint Survey, feds show they're feeling better than they have over the past few years – but there's still room for improvement, especially in mission-critical IT.
The General Services Administration's Enterprise Infrastructure Solutions contract will anchor the agency's NS 2020 telecommunications strategy.
House and Senate both approve a measure to maintain funding at fiscal 2015 levels through Dec. 11.
The aviation regulator missed a Congressional deadline to develop rules of the sky-ways for drones.
Agency heads, acquisition officers and even CIOs don't need to be hard-core techies. FCW's four-part look at the expertise that's really required to make federal IT run.
FCW recognizes 14 individuals having an outsized impact early in their careers.
As Steve Kelman returns from a summer of aggressive medical treatment, he considers the all-too-common aversion to short-term sacrifices.
8609 Westwood Center Drive, Suite 500Vienna, VA 22182-2215 703-876-5100
© 1996-2013 1105 Media, Inc. All Rights Reserved. This copy is for your personal, non-commercial use only.To order presentation-ready copies for distribution to colleagues, clients or customers, visit: www.1105Reprints.com


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Release Date:
October 23, 2012
Michael Jackson The Experience is a dance and party game for Nintendo DS and DSi that allows players on the small screen to get into the groove and in some small sense, perhaps attain the level of the King of Pop himself. Designed as a complete Michael Jackson gameplay experience, the game features a wealth of unforgettable hits, touchscreen rhythm input of authentic handheld versions of Michael's dance moves using the DS' stylus, and game environments reminiscent of some of Michael Jackson's most popular live performances and music videos.
This game was Pre-Owned. Any offers, bonus content and online play may not be available with the purchase of this product or may require additional fees.
Video Game Condition: Pre-Owned;
- Genre: Dance
- Platform: Nintendo DS
other Info.
- Online Item #: 14321203
- Store Item Number (DPCI): 207-35-0185
- Made in the USA