text:
|Publication number
|Publication type
|Application number
|Publication date
|Nov 7, 2006
|Filing date
|Apr 26, 2005
|Priority date
|Nov 30, 1995
|Fee status
|Also published as
|Publication number
|James W. Douglass, Thomas J. Riding, James E. Ring
|Original Assignee
|Clarient, Inc.
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (103), Non-Patent Citations (36), Referenced by (32), Classifications (29), Legal Events (7)
|External Links: USPTO, USPTO Assignment, Espacenet
This application is a continuation of U.S. patent application Ser. No. 10/404,921, filed Mar. 31, 2003 and now U.S. Pat. No. 6,920,239 which is a continuation of U.S. patent application Ser. No. 09/492,101, filed Feb. 14, 2000 and now U.S. Pat. No. 6,553,135, which is a continuation of U.S. patent application Ser. No. 08/758,436, filed Nov. 27, 1996 and now U.S. Pat. No. 6,215,892, which claims the benefit of U.S. Provisional Application No. 60/026,805, filed Nov. 30,1995.
In the field of medical diagnostics including oncology, the detection, identification, quantitation and characterization of cells of interest, such as cancer cells, through testing of biological specimens is an important aspect of diagnosis. Typically, a biological specimen such as bone marrow, lymph nodes, peripheral blood, cerebrospinal fluid, urine, effusions, fine needle aspirates, peripheral blood scrapings or other materials are prepared by staining the specimen to identify cells of interest. One method of cell specimen preparation is to react a specimen with a specific probe which can be a monoclonal antibody, a polyclonal antiserum, or a nucleic acid which is reactive with a component of the cells of interest, such as tumor cells. The reaction may be detected using an enzymatic reaction, such as alkaline phosphatase or glucose oxidase or peroxidase to convert a soluble colorless substrate to a colored insoluble precipitate, or by directly conjugating a dye to the probe.
Examination of biological specimens in the past has been performed manually by either a lab technician or a pathologist. In the manual method, a slide prepared with a biological specimen is viewed at a low magnification under a microscope to visually locate candidate cells of interest. Those areas of the slide where cells of interest are located are then viewed at a higher magnification to confirm those objects as cells of interest, such as tumor or cancer cells. The manual method is time consuming and prone to error including missing areas of the slide.
Automated cell analysis systems have been developed to improve the speed and accuracy of the testing process. One known interactive system includes a single high power microscope objective for scanning a rack of slides, portions of which have been previously identified for assay by an operator. In that system, the operator first scans each slide at a low magnification similar to the manual method and notes the points of interest on the slide for later analysis. The operator then stores the address of the noted location and the associated function in a data file. Once the points of interest have been located and stored by the operator, the slide is then positioned in an automated analysis apparatus which acquires images of the slide at the marked points and performs an image analysis.
A problem with the foregoing automated system is the continued need for operator input to initially locate cell objects for analysis. Such continued dependence on manual input can lead to errors including cells of interest being missed. Such errors can be critical especially in assays for so-called rare events, e.g., finding one tumor cell in a cell population of one million normal cells. Additionally, manual methods can be extremely time consuming and can require a high degree of training to properly identify and/or quantify cells. This is not only true for tumor cell detection, but also for other applications ranging from neutrophil alkaline phosphatase assays, reticulocyte counting and maturation assessment, and others. The associated manual labor leads to a high cost for these procedures in addition to the potential errors that can arise from long, tedious manual examinations. A need exists, therefore, for an improved automated cell analysis system which can quickly and accurately scan large amounts of biological material on a slide. Accordingly, the present invention provides a method and apparatus for automated cell analysis which eliminates the need for operator input to locate cell objects for analysis.
In accordance with the present invention, a slide prepared with a biological specimen and reagent is placed in a slide carrier which preferably holds four slides. The slide carriers are loaded into an input hopper of the automated system. The operator may then enter data identifying the size, shape and location of a scan area on each slide, or, preferably, the system automatically locates a scan area for each slide during slide processing. The operator then activates the system for slide processing. At system activation, a slide carrier is positioned on an X-Y stage of an optical system. Any bar codes used to identify slides are then read and stored for each slide in a carrier. The entire slide is rapidly scanned at a low magnification, typically 10�. At each location of the scan, a low magnification image is acquired and processed to detect candidate objects of interest. Preferably, color, size and shape are used to identify objects of interest. The location of each candidate object of interest is stored.
At the completion of the low level scan for each slide in the carrier on the stage, the optical system is adjusted to a high magnification such as 40� or 60�, and the X-Y stage is positioned to the stored locations for the candidate objects of interest on each slide in the carrier. A high magnification image is acquired for each candidate object of interest and a series of image processing steps are performed to confirm the analysis which was performed at low magnification. A high magnification image is stored for each confirmed object of interest. These images are then available for retrieval by a pathologist or cytotechnologist to review for final diagnostic evaluation. Having stored the location of each object of interest, a mosaic comprised of the candidate objects of interest for a slide may be generated and stored. The pathologist or cytotechnologist may view the mosaic or may also directly view the slide at the location of an object of interest in the mosaic for further evaluation. The mosaic may be stored on magnetic media for future reference or may be transmitted to a remote site for review and/or storage. The entire process involved in examining a single slide takes on the order of 2–15 minutes depending on scan area size and the number of detected candidate objects of interest.
The present invention has utility in the field of oncology for the early detection of minimal residual disease ("micrometastases"). Other useful applications include prenatal diagnosis of fetal cells in maternal blood and in the field of infectious diseases to identify pathogens and viral loads, alkaline phosphatase assessments, reticulocyte counting, and others.
The processing of images acquired in the automated scanning of the present invention preferably includes the steps of transforming the image to a different color space; filtering the transformed image with a low pass filter, dynamically thresholding the pixels of the filtered image to suppress background material; performing a morphological function to remove artifacts from the thresholded image; analyzing the thresholded image to determine the presence of one or more regions of connected pixels having the same color, and categorizing every region having a size greater than a minimum size as a candidate object of interest.
According to another aspect of the invention, the scan area is automatically determined by scanning the slide; acquiring an image at each slide position; analyzing texture information of each image to detect the edges of the specimen; and storing the locations corresponding to the detected edges to define the scan area.
According to yet another aspect of the invention, automated focusing of the optical system is achieved by initially determining a focal plane from an array of points or locations in the scan area. The derived focal plane enables subsequent rapid automatic focusing in the low power scanning operation. The focal plane is determined by determining proper focal positions across an array of locations and performing an analysis such as a least squares fit of the array of focal positions to yield a focal plane across the array. Preferably, a focal position at each location is determined by incrementing the position of a Z stage for a fixed number of coarse and fine iterations. At each iteration, an image is acquired and a pixel variance or other optical parameter about a pixel mean for the acquired image is calculated to form a set of variance data. A least squares fit is performed on the variance data according to a known function. The peak value of the least squares fit curve is selected as an estimate of the best focal position.
In another aspect of the present invention, another focal position method for high magnification locates a region of interest centered about a candidate object of interest within a slide which were located during an analysis of the low magnification images. The region of interest is preferably n columns wide, where n is a power of 2. The pixels of this region are then processed using a Fast Fourier Transform to generate a spectra of component frequencies and corresponding complex magnitude for each frequency component. Preferably, the complex magnitude of the frequency components which range from 25% to 75% of the maximum frequency component are squared and summed to obtain the total power for the region of interest. This process is repeated for other Z positions and the Z position corresponding to the maximum total power for the region of interest is selected as the best focal position. This process is preferably used to select a Z position for regions of interest for slides containing neutrophils stained with Fast Red to identify alkaline phosphatase in cell cytoplasm and counterstained with hemotoxylin to identify the nucleus of the neutrophil cell. This focal method may be used with other stains and types of biological specimens, as well.
According to still another aspect of the invention, a method and apparatus for automated slide handling is provided. A slide is mounted onto a slide carrier with a number of other slides side-by-side. The slide carrier is positioned in an input feeder with other slide carriers to facilitate automatic analysis of a batch of slides. The slide carrier is loaded onto the X-Y stage of the optical system for the analysis of the slides thereon. Subsequently, the first slide carrier is unloaded into an output feeder after automatic image analysis and the next carrier is automatically loaded.
The file of this patent contains at least one drawing executed in color. Copies of this patent with color drawings will be provided by the Patent and Trademark Office upon request and payment of the necessary fee.
The above and other features of the invention including various novel details of construction and combinations of parts will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular apparatus embodying the invention is shown by way of illustration only and not as a limitation of the invention. The principles and features of this invention may be employed in varied and numerous embodiments without departing from the scope of the invention.
Referring now to the figures, an apparatus for automated cell analysis of biological specimens is generally indicated by reference numeral 10 as shown in perspective view in
The purpose of the apparatus 10 is for the unattended automatic scanning of prepared microscope slides for the detection and counting of candidate objects of interest such as normal and abnormal cells, e.g., tumor cells. The preferred embodiment may be utilized for rare event detection in which there may be only one candidate object of interest per several hundred thousand normal cells, e.g., one to five candidate objects of interest per 2 square centimeter area of the slide. The apparatus 10 automatically locates and counts candidate objects of interest and estimates normal cells present in a biological specimen on the basis of color, size and shape characteristics. A number of stains are use to preferentially stain candidate objects of interest and normal cells different colors so that such cells can be distinguished from each other.
As noted in the background of the intention, a biological specimen may be prepared with a reagent to obtain a colored insoluble precipitate. The apparatus of the present invention is used to detect this precipitate as a candidate object of interest.
During operation of the apparatus 10, a pathologist or laboratory technician mounts prepared slides onto slide carriers. A slide carrier 60 is illustrated in
After the low magnification scanning is completed, the apparatus automatically returns to each candidate cell, reimages and refocuses at a higher magnification such as 40� and performs further analysis to confirm the cell candidate. The apparatus stores an image of the cell for later review by a pathologist All results and images can be stored to a storage device 21 such as a removable hard drive or DAT tape or transmitted to a remote site for review or storage. The stored images for each slide can be viewed in a mosaic of images for further review. In addition, the pathologist or operator can also directly view a detected cell through the microscope using the included oculars 20 or on image monitor 27.
Having described the overall operation of the apparatus 10 from a high level, the further details of the apparatus will now be described. Referring to
The system processor 102 further controls an illumination controller 106 for control of substage illumination 48. The light output from the halogen light bulb which supplies illumination for the system can vary over time due to bulb aging, changes in optical alignment, and other factors. In addition, slides which have been "over stained" can reduce the camera exposure to an unacceptable level. In order to compensate for these effects, the illumination controller 106 is included. This controller is used in conjunction with light control software to compensate for the variations in light level. The light control software samples the output from the camera at intervals (such as between loading of slide carriers), and commands the controller to adjust the light level to the desired levels. In this way, light control is automatic and transparent to the user and adds no additional time to system operation.
The system processor 23 is preferably comprised of dual parallel Intel Pentium 90 MHz devices. The image processor 25 is preferably a Matrox Imaging Series 640 model. The microscope controller system processor 102 is an Advanced Micro Devices AMD29K device.
Referring now to
Vibration isolation mounts 40, shown in further detail in
The automatic slide handling feature of the present invention will now be described. The automated slide handling subsystem operates on a single slide carrier at a time. A slide carrier 60 is shown in
The slide input module 15 comprises a slide carrier input hopper 16, loading platform 52 and slide carrier loading subassembly 54. The input hopper 16 receives a series of slide carriers 60 (
The input module 15 further includes a revolving indexing cam 56 and a switch 90 mounted in the loading platform 52, the operation of which is described further below. The carrier loading subassembly 54 comprises an infeed drive belt 59 driven by a motor 86. The infeed drive belt 59 includes a pusher tab 58 for pushing the slide carrier horizontally toward the X-Y stage 38 when the belt is driven. A homing switch 95 senses the pusher tab 58 during a revolution of the belt 59.
Referring specifically to
The slide output module 17 includes slide carrier output hopper 18, unloading platform 36 and slide carrier unloading subassembly 34. The unloading subassembly 34 comprises a motor 89 for rotating the unloading platform 36 about shaft 98 during an unloading operation described further below. An outfeed gear 93 driven by motor 88 rotatably engages the pitch rack 68 of the carrier 60 (
The slide handling operation will nor be described. Referring to
Referring again to
The unloading operation is shown in more detail in end views of the output module 17 (
Having described the overall system and the automated slide handling feature, the aspects of the apparatus 10 relating to scanning, focusing and image processing will now be described in further detail.
In some cases, an operator will know ahead of time where the scan area of interest is on the slide. Conventional preparation of slides for examination provides repeatable and known placement of the sample on the slide. The operator can therefore instruct the system to always scan the same area at the same location of every slide which is prepared in this fashion. But there are other times in which the area of interest is not known, for example, where slides are prepared manually with a known smear technique. One feature of the invention automatically determines the scan area using a texture analysis process.
At each location of this raster scan, an image such as in
In the interest of speed, the texture analysis process is performed at a lower magnification, preferably at a 4� objective. One reason to operate at low magnification is to image the largest slide area at any one time. Since cells do not yet need to be resolved at this stage of the overall image analysis, the 4� magnification is preferred. On a typical slide, as shown in
Texture values for each window include the pixel variance over a window, the difference between the largest and smallest pixel value within a window, and other indicators. The presence of a smear raises the texture values compared with a blank area.
One problem with a smear from the standpoint of determining its location is its non-uniform thickness and texture. For example, the smear is likely to be relatively thin at the edges and thicker towards the middle due to the nature of the smearing process. To accommodate for the non-uniformity, texture analysis provides a texture value for each analyzed area. The texture value tends to gradually rise as the scan proceeds across a smear from a thin area to a thick area, reaches a peak, and then falls off again to a lower value as a thin area at the edge is reached. The problem is then to dec de from the series of texture values the beginning and ending, or the edges, of the smear. The texture values are fit to a square wave waveform since the texture data does not have sharp beg beginnings and endings.
After conducting this scanning and texture evaluation operation, one must determine which areas of elevated texture values represent the desired smear 74, and which represent undesired artifacts. This is accomplished by fitting a step function, on a line by line basis to the texture values in step 216. Tis function, which resembles a single square wave across the smear with a beginning at one edge, and end at the a other edge, and an amplitude provides the means for discrimination. The amplitude of the best-fit step function is utilized to determine whether smear or dirt is present since relatively high values indicate smear. If it is decided that smear is present, the beginning and ending coordinates of this pattern are noted until all lines have been processed, and the smear sample area defined at 218.
After an initial focusing operation described further herein, the scan area of interest is scanned to acquire images for image analysis. The preferred method of operation is to initially perform a complete scan of the slide at low magnification to identify and locate candidate objects of interest, followed by further image analysis of the candidate objects of interest at high magnification in order to confirm the object; as cells. An alternate method of operation is to perform high magnification image analysis of each candidate object of interest immediately after the object has been identified at low magnification. The low magnification scanning then resumes, searching for additional candidate objects of interest. Since it takes on the order of a few seconds to change objectives, this alternate method of operation would take longer to complete.
The operator can pre-select a magnification level to be used for the scanning operation. A low magnification using a 10� objective is preferred for the scanning operation since a larger area can be initially analyzed for each acquired scan image. The overall detection process for a cell includes a combination of decisions made at both low (10�) and high magnification (40�) levels. Decision making at the 10� magnification level is broader in scope, i.e., objects that loosely fit the relevant color, size and shape characteristics are identified at the 10� level. Analysis at the 40� magnification level then proceeds to refine the decision making and confirm objects as likely cells or candidate objects of interest. For example, at the 40� level it is not uncommon to find that some objects that were identified at 10� are artifacts which the analysis process will then reject. In addition, closely packed objects of interest appearing at 10� are separated at the 40� level.
In a situation where a cell straddles or overlaps adjacent image fields, image analysis of the individual adjacent image fields could result in the cell being rejected or undetected. To avoid missing such cells, the scanning operation compensates by overlapping adjacent image fields in both the x and y directions. An overlap amount greater than half the diameter of an average cell is preferred. In the preferred embodiment, the overlap is specified as a percentage of the image field in the x and y directions.
The time to complete an image analysis can vary depending upon the size of the scan area and the number of candidate cells, or objects of interest identified. For one example, in the preferred embodiment, a complete image analysis of a scan area of two square centimeters in which 50 objects of interest are confirmed can be performed in about 12 to 15 minutes. This example includes not only focusing, scanning and image analysis but also the saving of 40� images as a mosaic on hard drive 21 (
Consider the utility of the present invention in a "rare event" application where there may be one, two or a very small number of cells of interest located somewhere on the slide. To illustrate the nature of the problem by analogy, if one were to scale a slide to the size of a football field, a tumor cell, for example, would be about the size of a bottle cap. The problem is then to rapidly search the football field and find the very small number of bottle caps and have a high certainty that none have been missed.
However the scan area is defined, an initial focusing operation must be performed on each slide prior to scanning. This is required since slides differ, in general, in their placement in a carrier. These differences include slight (but significant) variations of tilt of the slide in its carrier. Since each slide must remain in focus during scanning, the degree of tilt of each slide must be determined. This is accomplished with an initial focusing operation that determines the exact degree of tilt, so that focus can be maintained automatically during scanning.
The initial focusing operation and other focusing operations to be described later utilize a focusing method based on processing of images acquired by the system. This method was chosen for its simplicity over other methods including use of IR beams reflected from the slide surface and use of mechanical gauges. These other methods also would not function properly when the specimen is protected with a coverglass. The preferred method results in lower system cost and improved reliability since no additional parts need be included to perform focusing.
These features include the determination of pixel variance at a relatively coarse number of focal positions, and then the fitting of a curve to the data to provide a faster means of determining optimal focus. This basic process is applied in two steps, coarse and fine.
During the coarse step at 220–230, the Z stage is stepped over a user-specified range of focus positions, with step sizes that are also user-specified. It has been found that for coarse focusing, these data are a close fit to a Gaussian function. Therefore, this initial set of variance versus focus position data are least-squares fit to a Gaussian function at 228. The location of the peak of this Gaussian curve determines the initial or coarse estimate of focus position for input to step 232.
Following this, a second stepping operation 232–242 is performed utilizing smaller steps over a smaller focus range centered on the coarse focus position. Experience indicates that data taken over this smaller range are generally best fit by a second order polynomial. Once this least squares fit is performed at 240, the peak of the second order curve provides the fine focus position at 244.
After determination of the best-fit focus plane, the scan area is scanned in an X raster scan over the scan area as described earlier. During scanning, the X stage is positioned to the starting point of the scan area, the focus (Z) stage is positioned to the best fit focus plane, an image is acquired and processed as described later, and this process is repeated for all points over the scan area. In this way, focus is maintained automatically without the need for time-consuming refocusing at points during scanning.
Prior to confirmation of cell objects at a 40� or 60� level, a refocusing operation is conducted since the use of this higher magnification requires more precise focus than the best-fit plane provides.
It should be noted that for some combinations of cell staining characteristics, improved focus can be obtained by numerically selecting the focus position that provides the largest variance, as opposed to selecting the peak of the polynomial. In such cases, the polynomial is used to provide an estimate of best focus, and a final step selects the actual Z position giving highest pixel variance. It should also be noted that if at any time during the focusing process at 40� or 60� the parameters indicate that the focus position is inadequate, the system automatically reverts to a coarse focusing process as described above with reference to
For some biological specimens and stains, the focusing methods discussed above do not provide optimal focused results. For example, certain white blood cells known as neutrophils may be stained with Fast Red, a commonly known stain, to identify alkaline phosphatase in the cytoplasm of the cells. To further identify these cells and the material within them, the specimen may be counterstained with hemotoxylin to identify the nucleus of the cells. In cells so treated, the cytoplasm bearing alkaline phosphatase becomes a shade of red proportionate to the amount of alkaline phosphatase in the cytoplasm and the nucleus becomes blue. However, where the cytoplasm and nucleus overlap, the cell a pears purple. These color combinations appear to preclude the finding of a focused Z position using the focus processes discussed above.
In an effort to find a best focal position at high magnification, a focus method, such as the one shown in
After the region of interest is selected, the columns of pixel values are processed using the preferred one dimensional FFT to determine a spectra of frequency components for the region of interest (Block 252). The frequency spectra ranges from DC to some highest frequency component. For each frequency component, a complex magnitude is computed. Preferably, the complex magnitudes for the frequency components which range from approximately 25% of the highest component to approximately 75% of the highest component are squared and summed to determine the total power for the region of interest (Block 254). Alternatively, the region of interest may be processed with a smoothing window, such as a Hanning window, to reduce the spurious high frequency components generated by the FFT processing of the pixel values in the region of interest. Such preprocessing of the region of interest permits all complex magnitude over the complete frequency range to be squared and summed. After the power for a region has been computed and stored (Block 256), a new focal position is selected, focus adjusted (Blocks 258, 260), and the process repeated. After each focal position has been evaluated, the one having the greatest power factor is selected as the one best in focus (Block 262).
The following describes the image processing methods which are utilized to decide whether a candidate object of interest such as a stained tumor cell is present in a given image, or field, during the scanning process. Candidate objects of interest which are detected during scanning are reimaged at higher (40� or 60�) magnification, the decision confirmed, and a region of interest for this cell saved for later review by the pathologist.
The image processing includes color space conversion, low pass filtering, background suppression, artifact suppression, morphological processing, and blob analysis. One or more of these steps can optionally be eliminated. The operator is provided with an option to configure the system to perform any or all of these steps and whether to perform certain steps more than once or several times in a row. It should also be noted that the sequence of steps may be varied and thereby optimized for specific reagents or reagent combinations; however, the sequence described herein is preferred. It should be noted that the image processing steps of low pass filtering, thresholding, morphological processing, and blob analysis are generally known image processing building blocks.
An overview of the preferred process is shown in
Neural net processing schemes were not considered for the preferred embodiment for several reasons. Firstly, the preferred embodiment is optimized for "rare-event" detection, although it is not limited to this case. Since neural nets must be trained on what to look for, sometimes several thousands Of examples rust be presented to the neural net for this training. This is impractical for a rare-event application. Secondly, neural net processing can be slower than "deterministic" methods, sometimes by large factors. Therefore, neural nets were not deemed appropriate for this application, although certain features of the invention may be advantageously applied to neural network systems.
In general, the candidate objects of interest, such as tumor cells, are detected based on a combination of characteristics, including size, shape, and color. The chain of decision making based on these characteristics preferably begins with a color space conversion process. The CCD camera coupled to the microscope subsystem outputs a color image comprising a matrix of 640�480 pixels. Each pixel comprises red, green and blue (RGB) signal values.
It is desirable to transform the matrix of RGB values to a different color space because the difference between candidate objects of interest and their background, such as tumor and normal cells, may be determined from their respective colors. Specimens are generally stained with one or more industry standard stains (e.g., DAB, New Fuchsin, AEC) which are "reddish" in color. Candidate objects of interest retain more of the stain and thus appear red while normal cells remain unstained. The specimens may also be counterstained with hematoxalin so the nuclei of normal cells or cells not containing an object of interest appear blue. In addition to these objects, dirt and debris can appear as black, gray, or can also be lightly stained red or blue depending on the staining procedures utilized. The residual plasma or other fluids also present on a smear may also possess some color.
In the color conversion operation, a ratio of two of the RGB signal values is formed to provide a means for discriminating color information. With three signal values for each pixel, nine different ratios can be formed:
The optimal ratio to select depends upon the range of color information expected in the slide specimen. As noted above, typical stains used for detecting candidate objects of interest such as tumor cells are predominantly red, as opposed to predominantly green or blue. Thus, the pixels of a cell of interest which has been stained contain a red component which is larger than either the green or blue components. A ratio of red divided by blue (R/B) provides a value which is greater than one for tumor cells but is approximately one for any clear or white areas on the slide. Since the remaining cells, i.e., normal cells, typically are stained blue, the R/B ratio for pixels of these latter cells yields values of less than one. The R/B ratio is preferred for clearly separating the color information typical in these applications.
Once the look up table is built at 304 for the specific color ration (i.e., choices of tumor and nucleated cell stains), each pixel in the original RGB image is converted at 308 to produce the output. Since it is of interest to separate the red stained tumor cells from blue stained normal ones, the ratio of color values is then scaled by a user specified factor. As an example, for a factor of 128 and the ratio of (red pixel value)/(blue pixel value), clear areas on the slide would have a ratio of 1 scaled by 128 for a final X value of 128. Pixels which lie in red stained tumor cells would have X value greater than 128, while blue stained nuclei of normal cells would have value less than 128. In this way, the desired objects of interest can be numerically discriminated. The resulting 640�480 pixel matrix, referred to as the X-image, is a gray scale image having values ranging from 0 to 255.
Other methods exist for discriminating color information. One classical method converts the RGB color information into another color space, such as HSI (hue, saturation, intensity) space. In such a space, distinctly different hues such as red, blue, green, yellow, may be readily separated. In addition, relatively lightly stained objects may be distinguished from more intensely stained ones by virtue of differing saturations. However, converting from RGB space to HSI space requires more complex computation. Conversion to a color ratio is faster, for example, a full image can be converted by the ratio technique of the present invention in about 30 ms while an HSI conversion can take several seconds.
In yet another approach, one could obtain color information by taking a single color channel from the camera. As an example, consider a blue channel, in which objects that are red are relatively dark. Objects which are blue, or white, are relatively light in the blue channel. In principle, one could take a single color channel, and simply set a threshold wherein everything darker than some threshold is categorized as a candidate object of interest, for example, a tumor cell, because it is red and hence dark in the channel being reviewed. However, one problem with the single channel approach occurs where illumination is not uniform. Non-uniformity of illumination results in non-uniformity across the pixel values in any color channel, for example, tending to peak in the middle of the image and dropping off at the edges where the illumination falls off. Performing thresholding on this non-uniform color information runs into problems, as the edges sometimes fall below the threshold, and therefore it becomes more difficult to pick the appropriate threshold level. However, with the ratio technique, if the values of the red channel fall off from center to edge, then the values of the blue channel also fall off center to edge, resulting in a uniform ratio. Thus, the ratio technique is more immune to illumination non-uniformities.
As previously described, the color conversion scheme is relatively insensitive to changes in color balance, i.e., the relative outputs of the red, green, and blue channels. However, some control is necessary to avoid camera saturation, or inadequate exposures in any one of the color bands. This color balancing is performed automatically by utilizing a calibration slide consisting of a clear area, and a "dark" area having a known optical transmission or density. The system obtains images from the clear and "dark" areas, calculates "white" and "black" adjustments for the image processor 25, and thereby provides correct color balance.
In addition to the color balance control, certain mechanical alignments are automated in this process. The center point in the field of view for the various microscope objectives as measured on the slide can vary by several (or several tens of) microns. This is the result of slight variations in position of the microscope objectives 44 a as determined by the turret 44 (
This is accomplished by imaging a test slide which contains a recognizable feature or mark. An image of this pattern is obtained by the system with a given objective, and the position of the mark determined. The system then rotates the turret to the next lens objective, obtains an image of the test object, and its position is redetermined. Apparent changes in position of the test mark are recorded for this objective. This process is continued for all objectives.
Once these spatial offsets have been determined, they are automatically compensated for by moving the stage 38 by an equal (but opposite) amount of offset during changes in objective. In this way, as different lens objectives are selected, there is no apparent shift in center point or area viewed.
A low pass filtering process precedes thresholding. An objective of thresholding is to obtain a pixel image matrix having only candidate objects of interest, such as tumor cells above a threshold level and everything else below it. However, an actual acquired image will contain noise. The noise can take several forms, including white noise and artifacts. The microscope slide can have small fragments of debris that pick up color in the staining process and these are known as artifacts. These artifacts are generally small and scattered areas, on the order of a few pixels, which are above the threshold. The purpose of low pass filtering is to essentially blur or smear the entire color converted image. The low pass filtering process will smear artifacts more than larger objects of interest. such as tumor cells and thereby eliminate or reduce the number of artifacts that pass the thresholding process. The result is a cleaner thresholded image downstream.
In the low pass filter process, a 3�3 matrix of coefficients is applied to each pixel in the 640�480 x-image. A preferred coefficient matrix is as follows:
At each pixel location, a 3�3 matrix comprising the pixel of interest and its neighbors is multiplied by the coefficient matrix and summed to yield a single value for the pixel of interest. The output of this spatial convolution process is again a 640�480 matrix.
As an example, consider a case where the center pixel and only the center pixel, has a value of 255 and each of its other neighbors, top left, top, top right and so forth, have values of 0. This singular white pixel case corresponds to a small object. The result of the matrix multiplication and addition using the coefficient matrix is a value of 1/9 (255) or 28 for the center pixel, a value which is below the nominal threshold of 128. Now consider another case in which all the pixels have a value of 255 corresponding to a large object. Performing the low pass filtering operation on a 3�3 matrix for this case yields a value of 255 for the center pixel. Thus, large objects retain their values while small objects are reduced in amplitude or eliminated. In the preferred method of operation, the low pass filtering process is performed on the X image twice in succession.
In order to separate objects of interest, such as a tumor cell in the x image from other objects and background, a thresholding operation is performed designed to set pixels within cells of interest to a value of 255, and all other areas to 0. Thresholding ideally yields an image in which cells of interest are white and the remainder of the image is black. A problem one faces in thresholding is where to set the threshold level. One cannot simply assume that cells of interest are indicated by any pixel value above the nominal threshold of 128. A typical imaging system may use and incandescent halogen light bulb as a light source. As the bulb ages, the relative amounts of red and blue output can change. The tendency as the bulb ages is for the blue to drop off more than the red and the green. To accommodate for this light source variation over time, a dynamic thresholding process is used whereby the threshold is adjusted dynamically for each acquired image. Thus, for each 640�480 image, a single threshold value is derived specific to that image.
As shown in
As is often the case with thresholding operations such as that described above, some undesired areas will lie above the threshold value due to noise, small stained cell fragments, and other artifacts. It is desired and possible to eliminate these artifacts by virtue of their small size compared with legitimate cells of interest. Morphological processes are utilized to perform this function.
Morphological processing is similar to the low pass filter convolution process described earlier except that it is applied to a binary image. Similar to spatial convolution, the morphological process traverses an input image matrix, pixel by pixel, and places the processed pixels in an output matrix. Rather than calculating a weighted sum of neighboring pixels as in the low pass convolution process, the morphological process uses set theory operations to combine neighboring pixels in a nonlinear fashion.
Erosion is a process whereby a single pixel layer is taken away from the edge of an object. Dilation is the opposite process which adds a single pixel layer to the edges of an object. The power of morphological processing is that it provides for further discrimination to eliminate small objects that have survived the thresholding process and yet are not likely tumor cells. The erosion and dilation processes that make up a morphological "open" preferably make small objects disappear yet allows large objects to remain. Morphological processing of binary images is described in detail in "Digital Image Processing", pages 127–137, G. A. Baxes, John Wiley & Sons, (1994).
At this point in the processing chain, the processed image contains thresholded objects of interest, such as tumor cells (if any were present in the original image), and possibly some residual artifacts that were too large to be eliminated by the processes above.
Comparison of the size of each blob to a known minimum area at 328 for a tumor cell allows a refinement in decisions about which objects are objects of interest, such as tumor cells, and which are artifacts. The location (x, y coordinates) of objects identified as cells of interest in this stage are saved for the final 40� reimaging step described below. Objects not passing the size test are disregarded as artifacts.
The processing chain described above identifies objects at the scanning magnification as cells of interest candidates. As illustrated in
As noted earlier, a mosaic of saved images is made available for viewing by the pathologist. As shown in
In addition to saving the image of the cell and its region, the cell coordinates are saved should the pathologist wish to directly view the cell through the oculars or on the image monitor. In this case, the pathologist reloads the slide carrier, selects the slide and cell for review from a mosaic of cell images, and the system automatically positions the cell under the microscope for viewing.
It has been found that normal cells whose nuclei have been stained with hematoxylin are often quite numerous, numbering in the thousands per 10� image. Since these cells are so numerous, and since they tend to clump, counting each individual nucleated cell would add an excessive processing burden, at the expense of speed, and would not necessarily provide an accurate count due to clumping. The apparatus performs an estimation process in which the total area of each field that is stained hematoxylin blue is measured and this area is divided by the average size of a nucleated cell.
In this process, a single color band (the red channel provides the best contrast for blue stained nucleated cells) is processed by calculating the average pixel value for each field at 342, establishing two threshold values (high and low) as indicated at 344, 346, and counting the number of pixels between these two values at 348. In the absence of dirt, or other opaque debris, this provides a count of the number of predominantly blue pixels. By dividing this value by the average area for a nucleated cell at 350, and looping over all fields at 352, an approximate cell count is obtained. Preliminary testing of this process indicates an accuracy with +/−15%. It should be noted that for some slide preparation techniques, the size of nucleated cells can be significantly larger than the typical size. The operator can select the appropriate nucleated cell size to compensate for these characteristics.
As with any imaging system, there is some loss of modulation transfer (i.e. contrast) due to the modulation transfer function (MTF) characteristics of the imaging optics, camera, electronics, and other components. Since it is desired to save "high quality" images of cells of interest both for pathologist review and for archival purposes, it is desired to compensate for these MTF losses.
An MTF compensation, or MTFC, is performed as a digital process applied to the acquired digital images. A digital filter is utilized to restore the high spatial frequency content of the images upon storage, while maintaining low noise levels. With this MTFC technology, image quality is enhanced, or restored, through the use of digital processing methods as opposed to conventional oil-immersion or other hardware based methods. MTFC is described further in "The Image Processing Handbook," pages 225 and 337, J. C. Rues, CRC Press (1995).
Referring to
In the analysis level 404, review 416 and report 418 functions are made available. At the review level 416, the operator can select a montage function 420. At this montage level, a pathologist can perform diagnostic review functions including visiting an image 422, accept/reject of cells 424, nucleated cell counting 426, accept/reject of cell counts 428, and saving of pages at 430. The report level 418 allows an operator to generate patient reports 432.
In the configuration level 406, the operator can select to configure preferences at 434, input operator information 437 at 436, create a system log at 438, and toggle a menu panel at 440. The configuration preferences include scan area selection functions at 442, 452; montage specifications at 444, bar code handling at 446, default cell counting at 448, stain selection at 450, and scan objective selection at 454.
While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
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|Microfluidic devices, and methods of making and using the same
|Aug 4, 2005
|Feb 8, 2007
|Perz Cynthia B
|Repositioning inaccuracies in an automated imaging system
|Jan 22, 2008
|Sep 4, 2008
|Becton, Dickinson And Company
|Method, system, and compositions for cell counting and analysis
|Jul 2, 2007
|Dec 11, 2008
|Aperio Technologies, Inc.
|System and Method for Assessing Image Interpretability in Anatomic Pathology
|Nov 9, 2007
|Mar 12, 2009
|Riverain Medical Group, Llc
|Presentation of computer-aided detection/diagnosis (CAD) results
|Jan 21, 2009
|Aug 27, 2009
|Tatsuki Yamada
|Microscope System, Image Generating Method, and Program for Practising the Same
|Feb 25, 2010
|Apr 19, 2012
|Nikon Corporation
|Incubated state evaluating device, incubated state evaluating method, incubator, and program
|Jun 6, 2008
|Dec 18, 2008
|Aperio Technologies, Inc.
|System and method for assessing image interpretability in anatomic pathology
|Jul 30, 2014
|Feb 5, 2015
|Express Diagnostics Int'l., Inc.
|Universal assay reader
|U.S. Classification
|382/128, 345/604
|International Classification
|Cooperative Classification
|European Classification
|Feb 9, 2006
Owner name: CLARIENT INC., CALIFORNIA
Free format text: MERGER;ASSIGNOR:CHROMAVISION MEDICAL SYSTEMS, INC.;REEL/FRAME:017240/0641
Effective date: 20050315
|Mar 17, 2006
Owner name: CHROMAVISION MEDICAL SYSTEMS, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DOUGLASS, JAMES W.;RIDING, THOMAS J.;RING, JAMES E.;REEL/FRAME:017350/0861
Effective date: 20000210
|Nov 7, 2007
Owner name: CARL ZEISS MICROIMAGING AIS, INC., NEW YORK
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CLARIENT, INC.;REEL/FRAME:020072/0662
Effective date: 20071016
|May 7, 2010
|Fee payment
Year of fee payment: 4
|Jun 20, 2014
|Maintenance fee reminder mailed
|Nov 7, 2014
|Lapse for failure to pay maintenance fees
|Dec 30, 2014
|Expired due to failure to pay maintenance fee
Effective date: 20141107


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This show we talk about some cool custom games people sent in, tales from the fox hole, weird gamertags, voice mails, shoutouts and more! With guest host Feel The Thunder.
Shout out and thanks to Expanded Flame for this week's album art. Suh-weet. (Did I get the artist right this time?)
Podcast: Play in new window | Download (11.4MB)
Subscribe: Apple Podcasts | Android | Google Play | RSS

subset: commoncrawl


text:
Edgewater Med Spa is your SkinCeuticals skincare professional and is your partner for healthy, beautiful skin. Dr. Taj Khan has been specially trained to create the ideal at-home and in-office skincare regimen that can help prevent accelerated skin aging, sun damage, and even skin cancer. Protect your skin with the best antioxidants and sunscreens available.
- Neutralizes UVA/UVB and pollution-included free radicals
- Replenishes natural antioxidant defenses depleted by persistent exposure to pollution and sun
- Diminishes wrinkles and improves elasticity and firmess
- Provides high UVA protection
- Protects with broad spectrum coverage
- Decreases UV-induced premature aging


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File:Drops I.jpg
Size of this preview: 640 × 480 pixels.
File usage
The following pages on Schools Wikipedia link to this image (list may be incomplete):
Learn more
Wikipedia for Schools is designed to make learning fun and easy. Thanks to SOS Children, 62,000 children are enjoying a happy childhood, with a healthy, prosperous future ahead of them. Will you help another child today?


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This man was shocked to discover that his longtime place of employment had a basement, but when he went to explore it, he really got the shock of his life.
When the man went down the stairs, he found "a random door at the bottom of the steps separate to the rest of the cellar."
There was no light in the basement, so the man couldn't see anything: until he took a picture with the flash on. It was then that he saw something truly creepy…
"Taking a picture with the flash on reveals this…..a coffin..behind that another door..nope. I'm done," the man recounted on reddit.
Understandably, the man took off running. We have to admit, we would do the same thing!


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Australian School Of Motoring
Drive your vehicle for nothingGet paid to driveThe cost of motoring keeps on going up. The price of a used car is determined depending on certain factors. Without any doubt, probably the most important document of any one who is driving can be a driving license. There are lots of different cars for you to definitely choose from. Trade in- the amount you'd be expecting to receive while trading inside the vehicle.
Watch the video and stay amazed. For these phones offer those instructors while providing cheap driving lessons in Lancaster is simply amazing. Additional Peace of Mind.
. It combines the features of the traditional hybrid and electric car, containing an electric motor plus a internal combustion engine (ICE) along with typical electric car characteristics being a plug for connecting towards the electric grid. Buying and Installation.
As the Blake Lapthorn Motoring Offences team explained in its previous article, if there's an innocent explanation for any vehicle not being insured (such because it has been stolen, scrapped or exported) then a registered keeper is obliged to notify the DVLA. You understand the drill, you appear back to when you were 10 years of age marvelling and dreaming each and every new car that is seen on the roads. . A much better option than dashboards and glove compartments if you constantly leave your phone in the car when you get into restaurants and http://www.ebay.com/sch/i.html?_nkw=mb%20motoring%20cap other places where you do not want to be disturbed.
K ended up driving his vehicle around the M40 motorway through Oxfordshire when he was stopped by an officer from Thames Valley Police. One day car insurance policies are intended to fulfill the short term and quick necessities of the motorists in the UK. As you can imagine the internet will form the very first a part of our search.
The Making. Many local takeaways pay through their noses to dispose of their chip fat. These motorists knows that their cars will be serviced, maintained and repaired, should this prove necessary, at no cost. A great deal of motorists are caught without the correct documents and so are held as convicts. It's not the situation that your insurance is invalid in the case of 'no insurance' offences, this is really a misnomer.


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In all the posts i read about the Traceva which has been given ood results in non samll cell lung cancer that especially for Adinocarcinoma. But i just want to know whether it work for Squamous cell carcinoma or not.
Does Traceva work for Squamous cell carcinoma?
- Asked
- 26 May 2011 by seekinghelp123
- Active
- 27 May 2011
- Topics
- tarceva, squamous cell carcinoma, cancer, lung cancer
Responses (1)
Hi, sorry i am unable to answer your question! I have researched it extensively and can't find anywhere that it has a positive effect on squamous cells in general. Maybe this is because it hasn't been trialled for this reason, although i find that hard to believe. I hope someone else can shed some light on your question.
Further Information
- Tarceva Information for Consumers
- Tarceva Information for Healthcare Professionals (includes dosage details)
- Side Effects of Tarceva (detailed)
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text:
In this fun activity, frozen cubes of paint provide your preschooler with a cool new way to paint a masterpiece.
Show your preschooler how to create a simple windsock using a colorful piece of tissue paper, a pipe cleaner, and yarn.
Preschoolers are fascinated by how colors can be mixed to form new ones. This activity combines color mixing with the concept of melting for extra science fun.
In this activity, your child fills jars with water to learn about acoustics, and practices mixing colors to form a rainbow of colors and sounds.
Sneak in a science lesson by creating your own parachute people using a handkerchief and a lightweight figurine.
Kids get innovative as they mix their own colors then make a painting with the new shades of color they've created.
Teach your preschooler about healthy foods and how to make a meal that is good for her with this fun activity that you both can do together.
How do mirrors work? What is light, and how does it travel? Explore light and reflection with your preschooler with these simple hands-on experiments.
This quick and simple preschool mirror activity will introduce your child to the science of reflection and light with a flashlight experiment and picture game.
With this activity, your child can write an invisible letter that will appear as if by magic when exposed to heat.
- 2


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Penn State DuBois legal issues
Think about what career is best suited for you, and where you want to study. Request more information now.
Think about what career is best suited for you, and where you want to study. Request more information now.
Think about what career is best suited for you, and where you want to study. Request more information now.
Get info about Penn State DuBois legal issues, and registering for elective seminars and discussion sections. As a student, your first stop should be the registrar's office, where you can pick up a free copy of both the academic calendar and course catalog, in order to find available credit classes that you may be interested in taking. You may be surprised that you can enroll for a full-time course load, without having to attend any 8am classes at all. Finally, check with your major department for assistance with prerequisites, tuition payment deadlines, and Penn State DuBois criminology information. Please use the form above to request admissions info for Penn State DuBois.
Legal careers can range from working in a legal setting such as a courtroom or law office to corporate consulting or tax planning. Preparing legal advice or representation for a client commonly requires substantial research into applicable laws and regulations. All lawyers in the US must have a law degree and must also pass their state?s written bar examination.
Most states and jurisdictions require lawyers to complete a juris doctor (JD) degree from a law school accredited by the American Bar Association (ABA), including courses such as constitutional law, contracts, property law, civil procedure, and legal writing. Students may complete an internship at a law firm, working under the supervision of an experienced attorney. Beyond consideration for Penn State DuBois legal issues, you may think about University of Evansville course requirements for complementary electives to your primary course of study.
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Law360, New York (May 13, 2011, 2:00 PM EDT) -- James L. Leonard is a partner in Barnes & Thornburg LLP's Atlanta office and practices in the firm's litigation department. He focuses his practice in the areas of insurance coverage advice and litigation on behalf of policyholders. He assists clients in recovering insurance proceeds in a variety of industries and professions, including airlines, auto parts, banking, technology, religious and nonprofit organizations, communications, construction, consumer products, credit management, hospitals, infrastructure, nursing home and long-term care facilities, physicians, property management, universities and many others. He also provides counseling...
Q&A With Barnes & Thornburg's Jim Leonard
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JOHANNESBURG, Oct 19 (Reuters) - South Africa is making progress towards avoiding rating downgrades despite the economy not growing fast enough, Finance Minister Pravin Gordhan told a trade union gathering on Wednesday a week before reading his mid-term budget speech.
Ratings agencies have warned they could cut the country's credit rating to "junk" by year-end if the country does not lift growth to at least 1 percent annually.
"As a country we have to understand that unless we solve the low growth problem, we don't have fiscal resources required for all needs," Gordhan said.
"The ratings agencies will say these promises that you guys made between March and June this year, you gave us milestones that you would cover, how much have you done? I believe we can begin to show a report card that does indicate some progress." (Reporting by Olivia Kumwenda-Mtambo; Writing by Mfuneko Toyana; Editing by James Macharia)


text:
Bill Roberts
Fort Worth, Texas, United States
I'm a financial restructuring professional, CPA, and Chief Financial Officer with 35 years of experience. A former partner of Corporate Revitalization (now Deloitte CRG), I have served as a senior financial officer for a wide variety of companies in distressed situations. My focus is small and mid-sized companies in the Dallas/Fort Worth area.


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And today's Look Of The Day goes to Ki and Ka and that is Kareena Kapoor and Arjun Kapoor for sporting the best desi outfits in summer fashion.
On Sunday, Kareena & Arjun were seen promoting their upcoming movie Ki&Ka. Kareena wore a vibrant yellow Indian suit with palazzo pants by Anushka Khanna and jewellery by Krsala Jewellery and juttis by Fizzy Goblet.
Arjun, on the other hand, wore a simple white kurta paired with a blue jeans. He added a grey scarf to enhance his look.
So guys, what do you think of this Monday Look Of The Day?


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I live and breath image making... taking inspiration from Nature. I am passionate about conserving the natural landscape to inspire future generations.
As a visual artist, I work with a variety of creative mediums, including, digital painting & illustration, location and studio photography, traditional watercolour & gouache painting, and pencil & ink drawing. I often combine elements from several of these disciplines, as the finished image dictates. I have never been a purist. The end result is what matters to me. My more illustrative work is strongly influenced by the traditions of impressionism & pointillism (Claude Monet, George Seurat & Gustav Klimt).
Based in beautiful Nidderdale, North Yorkshire, I derive much of my creativity from my local surroundings. Many of my paintings and photographs are inspired by my local landscape & artists - such as the poet Ted Hughes - whose inspired portrayals of this rugged Northern landscape accurately capture the essence of the natural world I have come to know and love.
My landscape photographs try to convey the silence and wonder I sense in the countryside throughout the seasons. I am particularly fond of the Winter months and love to capture the transformative natural beauty of snow, frost and ice.
My paintings explore the spirituality and essence of nature itself. My woodlands and forests are brimming with secrets, shaped by the seasons and weathered by the elements. My nightscapes are windows into the soul of mankind... full of faith, optimism, and a life-long yearning for knowledge. Star and moonlit skies filled with magic and wonder.
Represented globally by Getty Images, my photographs and paintings feature across a broad range of brochures, magazine and book covers. A large folio of my images are available for commercial license here: Getty Images.
My favourite paintings and photographs are available in small exclusive limited editions (20 prints only off my 34cm x 34cm / 46cm x 30.5cm medium image size and 10 prints only off each of the special larger 70cm x 70cm / 94cm x 62.5cm image size). Both editions are specially Giclee printed onto heavyweight 315 gsm smooth cotton high white archival fine art photo paper with an ample border. Each limited edition print run has been faithfully reproduced with great attention to detail by myself and my local fine art printer, checked and approved personally, signed, numbered and titled, together with an official certificate of authenticity. You can also purchase these through my on-line partnership galleries at Saatchi and Art.Finder.
I am more than happy to work with corporate clients and public institutions. With a large catalogue of over 4000 themed images available, I am able to put together a bespoke folio of prints tailored to your specific requirements, and in a variety of sizes and finishes. Preferential rates always offered for multiple orders.
Please feel free to contact me. I am always happy to discuss my work and help with any enquiries.
My work has already been chosen and featured by: ABC News, The Actuary, Adidas, Age UK, All About You, Allianz, Amazon, American Daily News, Apple, Asda, Axa, BBC, BBC Radio 4, Beauty High, Best Western, Bet.com, Bloomberg, Bon Appetit, Boots, Build, Blush, British Airways, British Comedy Guide, Business week, Buzzle, Canadian Business, CBS News, China IRN, Ciniplex, Clariant, CNBC, CNN, Conde Nast, Channel 4, Chatelaine France, Chicago Tribune, China Medical Tribune, Cloud Nine, Cosmopolitan, Culture Mom, Cupid's Pulse, The Daily Record, Dell, Deutschland Today, DHL, Die Welt, DTI, D.F.S, Ebay, E How, Elle Girl, The Economic Times, EdTech, Esquire, Estates Review, Eurohealth, The Examiner, Express Newspaper, Fedex, Pour Femme, FHM, Folha De S.Paulo, Forbes, Fortune, Format, France TV News, GHD, The Global Mail, Goldman, Google, GQ, Good Housekeeping, Green Flag, The Guardian, HBOS, Hitachi, The Herald, Huff Post, In Flight Catering Services, The Independent, India Times, Interflora, Irish Times, Italia and Mondo, iTunes, Jack FM, John Smith's Brewery, Knox News, KMBZ Kansas City, KPMG, Krispy Kreme, Law Actually, Life and Style, Lombard, Mail Online, Market Insider, Marie Claire, M & S, Marketplace, Men's Health, Men's Journal, Metro, Microsoft, Mirror Group, Money Advice Service, Money hacker, Le Monde, Morrisons plc, MSN, MTV, National Australia Group, Nature, The New Economy, New Scientist, NHS, N Power, New York Review, Otto, Panasonic, Paypal, Pfizer, Polityka.PL, Practical Parenting, Penguin Group, People.com China, Prince of Wales Hospice, Radio Australia, Radio Times, Radio 5 Live, RBS, RCS Capital, Redbook, La Repubblica, RHS, Sage, Samsung, Sainsbury's, Seattle PI, Siemens, Sky, Sky News, Somerfield, Speigel, Stern, Sunday Mail, Sunday Times, The Sun, Sugon, Sydney Morning Herald, Telemundo, Theakston's Brewery, De Telegraaf, The Telegraph, Time Magazine, The Times, Time Out, Tiscali, TLT News Russia, Top News Arab Emirates, Trading Standards UK, Traders International, Trends.com China, UK Trade and Investment, Walmart, The Wall Street Journal, Woman.com, Woman's Hour, Women's Health Encyclopedia, Women's weekly, La Vanguardia, Viacom, Verizon, Vodafone, Vodacom, Vogue, Yahoo, Yellow Pages, Yokamen.CN, Yorkshire Water, You and Your Wedding, YouMag, Youth.CN, Xerox.