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Hardware Acceleration of Retinal Blood Vasculature Segmentation
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1 Hardware Acceleration of Retinal Blood Vasculature Segmentation Dimitris Koukounis, Christos Ttofis, Theocharis Theocharides KIOS Research Center ECE Department, University of Cyprus 123 Kyreneias Ave, Nicosia, Cyprus {koukounis.dimitris, ttofis.christos, ABSTRACT Retinal vessel tree extraction is a complex and computationally intensive task used in several medical and biometric applications. The emergence of portable biometric authentication applications, as well as on-site biomedical diagnostics, raises the need for hardware-accelerated, power-efficient architectures that can satisfy the performance and accuracy requirements of retinal vessel tree extraction. As such, this paper presents a VLSI implementation of a retina vessel segmentation system, in an attempt to illustrate the advantages and performance benefits that result from a dedicated VLSI solution. The proposed design implements an unsupervised, vessel segmentation algorithm, which utilizes match filtering with signed integers to enhance the difference between the blood vessels and the rest of the retina. The design simplifies the process of obtaining a binary map of the vessel tree by using parallel processing and efficient resource sharing, thus offering real-time performance. FPGA-based simulation results indicate significant performance improvements (up to 90x) when compared to existing hardware and software implementations. Categories and Subject Descriptors B.7.1 [Integrated Circuits]: Types and Design Styles Algorithms implemented in hardware. General Terms Algorithms, Performance, Design. Keywords Retinal Vessel Segmentation, Portable Biometrics, On-site medical diagnostics, Reconfigurable Parallel Architectures, Hardware Acceleration. 1. INTRODUCTION Separating the blood vessel tree from the retina, is an important and daunting process in biometric and biomedical applications. As such, several algorithms have been implemented, mostly on software, that perform retinal blood vasculature segmentation, and most of them target medical applications where speed is extremely important [2]. These algorithms are typically used by physicians to diagnose and investigate different diseases that Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. GLSVLSI'13, May 2 3, 2013, Paris, France. Copyright /13/05...$ cause changes to the blood vasculature. Many of those algorithms manage to provide satisfactory results in terms of accuracy. However, the use of the retina in identification or authentication systems traditionally was rarely used due to the high cost of the image capturing equipment, but most importantly due to the complex computational demands [1]. Emerging research suggests hardware acceleration [3-5] that aims to speed up the processing time of vessel segmentation algorithms, thus realizing their use in biometric systems. However, to take advantage of the emerging need for portable biometric authentication devices, as well as on-site diagnostic requirements, further performance improvement is still necessary, with the power consumption an additional significant constraint. Portable biometric authentication systems and on-site medical diagnostic equipment require high performance that achieves realtime response, low resource requirements and an acceptable accuracy able to authenticate/identify an individual or detect accurately a disease. Existing hardware attempts have various insufficiencies in regard to the processing time and their scalability to high-resolution images, thus have partially satisfied these requirements so far. This paper therefore presents a VLSI implementation of an unsupervised vessel segmentation algorithm. The proposed hardware architecture adopts the main algorithmic idea from [12] that was implemented in software, but integrates hardware optimization techniques in an attempt to make the algorithm hardware-friendly while achieving high performance and accuracy. A matched filtering technique is used to augment the vessels from retinal images, which helps the extraction of the binary map of the vessel tree. The binary image can be used at a later stage in a segmentation-driven registration system in order to make the registration process more accurate. The paper presents several architectural optimizations stemming from the targeted hardware architecture, and thus is able to achieve significant performance increase (~90x) when compared to other hardware and software implementations, with acceptable accuracy (within ~4% of the highest reported accuracy achieved with hardware acceleration). The proposed architecture was implemented and evaluated on a Spartan 6 FPGA, in order to derive synthesis and performance evaluation results, yielding s per 768x584 pixels image. The paper is organized as follows. Section II presents background information and describes the algorithm used to segment the blood vessel tree. Section III summarizes and reviews the related work. Then the proposed hardware architecture is presented in Section IV and Section V presents the simulation results. Finally, this paper concludes in Section VI where conclusions and future directions are presented. 113
2 2. Background 2.1 Vessel Segmentation A large number of algorithms relating to the segmentation of the retinal blood vessels have been published so far, and can be classified into two broad categories: supervised and unsupervised [2]. Furthermore, [2] classifies the blood vessels segmentation algorithms in seven main categories based on the methodology followed: (1) pattern recognition techniques, (2) matched filtering (MF), (3) vessel tracking/tracing, (4) mathematical morphology, (5) multiscale approaches, (6) model based approaches and (7) parallel/hardware based approaches. Among the aforementioned categories, matched filtering approaches give the more accurate results [7], thus the proposed architecture is inspired by a matched filter approach, and specifically, the algorithm presented in [12]. This algorithm integrates matched filtering and a threshold probing technique to achieve the desirable results. As proposed in [8], which presented the design of a hardware image processing system based on matched filtering, this approach has another significant advantage; the filter is implemented using integers, rather than floating point values, making the algorithm hardware friendly. Fig. 1.(a) Retinal Image. (b) Matched Filter Response. 2.2 Matched filtering-based algorithm The matched filter describes the expected appearance of the blood vessels in retinal images. The design of the matched filter in [8] is based on three main characteristics of the blood vessels: the first characteristic is that the vessels have a small curvature so they can be approximated by piecewise linear segments, the second characteristic is that the vessels have a lower reflectance compared to the rest of the retinal and as a result vessels appear darker in comparison with the background, and lastly, the third feature of the vessels is that their width lies between 2-10 pixels. Utilizing these characteristics, [8] proposes a Gaussian function as a model for a blood vessel profile. In order to extend the model to two dimensions (X and Y), it is assumed that a vessel has fixed width and direction, for a short length. The matched filter is then rotated from 0 to 180 with a 15 step. The twelve resulting filter kernels are convolved with the image. The matched filter response (MFR) is the highest scoring value at each pixel. The MFR of a retinal image can be seen in Figure 1b. Once the convolution with the matched filter finishes, the vessels are well distinguished from the rest of the retina. The MFR is thresholded by using a threshold value calculated automatically through iterative thresholding, and selected based on the number of pixels that have the most common pixel value on the image. The next step involves threshold probing, where the segments created by the thresholding are used to locate a set of starting points to initialize the probe queue. Probing is iterative and is used to determine the appropriate threshold for the area being probed. 3. Related Work The majority of the algorithms presented in literature, have been implemented in software running on general-purpose processors or clusters. An ad-hoc parallel implementation for the segmentation of high-resolution images is presented in [6]. In this approach, the image is divided into sub-images that have overlapping regions and each sub-image is distributed across computers for feature extraction and region growing; the segmentation results are then combined. The proposed algorithm processes 7 megapixel images in less than two minutes, facing memory shortage issues stemming from the use of such highdefinition images. A similar parallel approach to segment highresolution images is proposed in [10]. This approach reports a processing time of 3.54s for segmenting a 10-megapixel image, at 92.5% accuracy (the accuracy is measured by the ratio of the total number of correctly classified pixels to the total number of pixels in the original image that actually belong to the vasculature). This approach spends 20% of the total execution time for communication and I/O purposes. In addition, [10] indicates that the use of multiprocessing computing platforms can enable the segmentation of high definition images in reasonable time. However, while easy to implement and extremely flexible, these approaches require state-of-the-art and expensive hardware platforms to execute, and consume excessive amount of energy. Therefore, the use of such systems can only be limited to certain medical applications, where power and cost are not an issue. A similar approach to the one described in this paper is shown in [21], and segments veins from medical video processing. It reports a performance of 215 frames per second (0.005s per image), however the performance value stated in [21] is theoretical and not verified experimentally. It relies only on the listed FPGA operating frequency, without considering issues that may affect performance such as I/O handling and memory accesses. The emergence of portable biometrics and on-site medical diagnosis, raised the need for dedicated hardware approaches, which have emerged recently in an attempt to overcome the constraints found in software-based solutions [2]. In [5], an FPGA-based implementation of vessel extraction is presented. The specific algorithm implements a single-instruction multiple data (SIMD) architecture on a Spartan-3 FPGA. It requires a total of 1.4s to extract the vessels from a 768x584 retinal image. Alonso-Montes et al. [4] present another hardware approach where the vessels are segmented by using local dynamic convolutions and morphological operations together with arithmetic and logical operations. These are implemented and tested in a fine-grained SIMD processor array. [4] reports an average accuracy of 91.8% and a processing time of 0.19s per image. While the hardware implementations in [3-5] achieve significant improvements in comparison with the software approaches, they partially satisfy the high performance requirement of portable systems that impose a satisfactory accuracy and most importantly vessel extraction in real-time response. Furthermore, most of the approaches follow a straight algorithm-to-hardware mapping, without any algorithmic optimizations or considerations for hardware-friendly design optimization techniques, especially focused on the reconfigurable fabric. As the segmentation process is the most computationally intensive part of the entire authentication or diagnosis procedure that involves retina examination, further performance improvement is necessary. As such, the work presented in this paper targets a hardware-based 114
3 Fig. 2. Proposed Architecture Overview Accuracy % Fig. 4. Accuracy vs Number of Matched Filters vessel segmentation approach that manages to achieve extremely high performance by reducing certain computationally intensive steps, sacrificing minimal (but acceptable) accuracy for performance speedup and power reduction. The hardware-targeted optimizations enable easy integration of the architecture in existing portable biomedical and biometric systems. 4. Proposed Hardware Architecture The proposed architecture consists of three major hardware units; the I/O unit, the Match Filter unit and the Threshold unit. These units are pipelined, thus are able to operate concurrently, and the interconnection between them is accomplished using on-chip buffers. Particularly, the system uses one scanline buffer to temporarily store the image data, and six smaller on-chip buffers to store the rotated matched filters. The complete system architecture and the communication flow between the units is shown in Fig. 2. It is assumed that input images are captured by a specialized retina camera, and are loaded through an external memory controller on the targeted hardware in a raster scan format. 4.1 I/O Unit No of MF The memory controller communicates with on-chip Block RAM memory and fetches the green channel values (8 bits) to the scanline buffer. The red and blue channels are not used as the green channel utilizes a better difference between the vessels and the retinal background [15]. The use of the scanline buffer provides manageable parallelism in order to continuously feed the processing units with the data needed without any delays. It allows to sequentially read the image from the BRAM, and uses it in parallel in order to exploit the available parallelism on the hardware platform. The only introduced delay by using this mechanism is an initial delay until the first N rows of the image are loaded (where N is the width of the kernel window) No of Bits Fig. 6. Accuracy vs Number of Bits per pixel Subsequently, this mechanism can provide data needed by the match filter unit every cycle. Accuracy % 4.2 Match Filter Unit (MFU) In contrast to the original software-based algorithm in [12], the number of the matched filters were reduced to 6 by increasing the rotation step from 15 to 30, in an attempt to reduce the computational complexity and memory requirements. Simulations indicate a less than 3% reduction in the accuracy when reducing the number of rotations. Figure 4 illustrates how the number of rotations used are affecting the accuracy of this vessel segmentation approach. When the rotation step is increased to 45 (4 matched filters) a significant reduction in accuracy is observed(less than 80%). At 30 the reduction in the accuracy is not significant (~3%) and the resulting accuracy of 90% is more than enough for the aimed applications. In addition, if more matched filters were used the hardware area overhead would increased significantly and thus increase the power consumption. The MFU performs the convolution between the image and the six resulting kernels (Fig.4a). Fig. 3 shows the pipeline stages followed during the convolution process. The first N lines of the image are being loaded to the scanline buffer, while the matched Fig. 3. Pipeline Stages followed in Match Filter Unit 115
4 a. b. Fig. 5. (a) Architecture of the Convolution Unit. (b) Architecture of the scaling done between the Convolution and Threshold Unit filter is rotated and stored in six different buffers. As soon as the scanline buffer fills up, the multiplexer selects the first kernel for pixel-wise multiplication. The number of parallel multiplications equals to the number of elements of the scanline buffer output window. Multiplication results are stored in a 16-bit buffer in order to have simultaneous access to all the elements, which are then all forwarded to the accumulation stage in the following clock cycle. At this point, the multiplexer selects the next kernel, and the operation iterates. Fig. 4a ("Tree Adder") shows the accumulation as the last step of the Convolution Unit, implemented as a tree adder, which enables a cost-effective implementation of the accumulator. The hardware layout resulting from this type of addition results in the highest performance and frequency achievable. The resulting accumulated 16-bit value is stored in a register and every time a new value is computed, a comparison between the previous value and the new value is done. Six values are compared, one for each kernel, and the maximum value remains as the final pixel value. The final pixel values are stored in another scanline buffer called the "Matched Filter Response". The MFR scanline buffer can be seen in the blood vessels segmentation unit, below the multiply-accumulate unit in Fig. 2. This process is repeated six times for every image window that the starting scan line buffer fetches, in order to convolve the image window with the six kernels (Fig. 5a). The pipelined architecture of the convolution unit allows it to sustain a valid pixel value every six clock cycles (given that convolution is done for all six kernels). This process is done for all windows in the image. The convolution unit can be easily modified to process more than one kernel in a single cycle, thus computing a valid pixel in less than 6 clock cycles, depending on the amount of parallelism desired, and the available hardware resources. 4.3 Threshold Unit Prior to thresholding, it is necessary to normalize the accumulation result of the pixels computed at the previous stage in order to simplify and reduce storage requirements. The authors in [12] normalize the resulting value by dividing each pixel with a constant value. To implement a hardware friendly normalization, we used only the 11 most significant bits of each pixel value (Fig. 2 Normalization Stage). Figure 6 displays the accuracy against the number of bits per pixel used. The accuracy reaches 90,07% which the highest achievable accuracy of the presented approach at 11 bits per pixel. At a lower number of bits, the accuracy is reduced considerably since important features of the vessels are ignored. As the number of bits is increased, the accuracy is reduced, as a result of the unwanted details included at such high numbers of bits per pixel. Moreover, the MFR consists of both positive and negative integer values; hence, all values are subsequently positively biased by the absolute value of the minimum pixel value, to create only positive integers and reduce the complexity of the threshold operation. This is described in Fig. 5b. The largest number from all the resulting pixels that have similar value (i.e. all characterize similar intensity in the image, which is usually the background and not the vasculature) is then used to compute the threshold value. As said, this value should represent the background of the retinal image. As shown in Fig. 1b, the background of the MFR is well distinguished from the vessels. The thresholding operation is done in parallel. All pixels above the threshold are replaced with '1' (white) and the rest of the pixels are replaced with '0' (black). 5. Experimental Evaluation and Results The proposed retinal blood vessel architecture was implemented on a Spartan 6 XC65LX150T FPGA, and was evaluated using images from publicly available databases such as the STARE database [12], the DRIVE database [11] and the MESSIDOR database [13]. The input retinal images were loaded into the BlockRAM of the FPGA and used as input to the system shown in Figure 2. The implemented system was evaluated subsequently in terms of performance, power consumption, accuracy and hardware requirements. Results of the obtained segmented images are shown in Figure 7, along with the corresponding software implementation results. 116
5 TABLE III. Processing speed and Accuracy Comparison for Various Systems Database Accuracy Frequency (MHz) Execution time (s) Platform Algorithm Chaudhuri et al [9] N/A N/A 2,0000 Special Image processing board MFR Hoover et al [7] STARE N/A 3,0000 Sun SPARCstation 20 MFR+Threshold Probing Alonso Montes et al [4] DRIVE N/A 0,1925 Cellular Neural Networks Pixel Level Snakes Cinsidiki and Aydun [8] DRIVE N/A 3,0000 Cluster Computer MFR+ANT algorithm A. Nieto et al [5] N/A 0, ,4000 FPGA Active Contour Proposed Architecture DRIVE 0, , 0313 FPGA MFR+Thresholding STARE 0, ,0318 FPGA MFR+Thresholding TABLE I. Image size and Execution time Image Size Number of Windows Total Execution Time (s) 768x , x , Performance The processing speed of the proposed hardware implementation is measured by the time (seconds) needed to extract the blood vessel tree of a retinal image. We used 20 images from the DRIVE [11] and STARE [12] database to compute an average processing time for the proposed architecture and the original software-based algorithm. Experimental results indicate that the proposed system offers a total performance acceleration of up to 90 times relative to the corresponding software implementation of the algorithm. Table I illustrates how the processing time of the proposed architecture is affected by the image size. A promising observation is that the processing time remains relatively small for a small image size (of course this is expected), but increases linearly as the image size increases, which is attributed to the I/O handling, since once the original set of pixels are loaded, processing is then dependent clearly on the amount of available FPGA resources. Given that the overall input image size plays a linear role in the initial scanline buffer filling up, the delay is then directly proportional to the overall number of input pixels per line, which explains the linear delay increase. This is attributed to the fact that the number of the windows being processed is equal to the number of pixels in the image (see Table I). Table III presents a comparison between existing implementations and the proposed architecture, showing the algorithm adopted in each work as well as the implementation platform. The proposed system is faster than all implementations, as it needs only s to extract the vessels from an image size of 768x584 while the processing time of the majority of the works listed in the table is comparatively lower (comparisons are based on the same image databases that were used in existing implementations). 5.2 Accuracy The accuracy of the system is defined as the ratio of the total number of correctly classified pixels (sum of true positives and true negatives) to the number of pixels in the image field of view. We compare the accuracy of the proposed architecture with the software-base implementation of the algorithm. The original algorithm has an accuracy of 92.67%, while the accuracy of the proposed architecture is 90.07%. This accounts for a quality drop of about 2.5%, and is attributed to the hardware optimizations TABLE II. Complete System Hardware Overheads Platform Spartan 6 XC65LX150T Slice LUTs (92152) Slice Registers (183304) DSP48A 1s (180) BRAMs (268) Freq. (MHz) 66% 14% 100% 55% 100 (kernel reduction and thresholding). While this might be a considerable drop for a high-end medical system, the accuracy drop-off is not as important in portable biometrics and biomedical diagnostics; furthermore, other algorithms can be used instead of thresholding in order to segment the blood vessels which can increase the accuracy. This, however, is left as future work. It is worth noting that only the work in [5] has been implemented on an FPGA platform, has an accuracy of 91%, which is roughly the same as our reported accuracy but with a total speed up in performance of up to 50x. 5.3 Area and Power Overheads The proposed FPGA-based vessel segmentation architecture yields promising processing speed. To determine its requirements in terms of the targeted FPGA, we implemented the experimental architecture on a Spartan 6 XC65LX150T FPGA. Table II gives synthesis results for the entire system implemented on the FPGA. The table lists area results for slice LUTs, slice registers and DSP components in order to give a complete picture of the required hardware overheads associated with the system. It can be observed that the entire system utilizes 66% of the FPGA LUTs and 14% of the FPGA slice registers. The slice LUTs are dominated by the convolution unit, which consumes 60%, while the slice registers are dominated by the scanline buffer. Additionally, the system takes full advantage of the DSPs available on the FPGA platform to perform most of the multiplication operations of the convolution unit. We used the Xilinx X-Power Analyzer tool and the image data as input to obtain the average dynamic consumption figures. The total power needed for one frame is just 2.146W, making the design suitable for embedded portable applications [19,20]. We anticipate that an ASIC implementation will further increase the performance while reducing the power as well. Conclusively, synthesis and performance results indicate that vessel segmentation from retinal images can be effectively implemented on reconfigurable hardware platforms, with high potential for biometric applications that require real-time processing speed. The proposed system consumes a relatively reasonable number of resources when implemented on a typical low-end FPGA such as the Spartan-6, leaving room for the host application, let that be biometric or biomedical, to expand the 117
6 a. b. c. d. e. f. Fig.7. (a) Original retinal image used as input to the system. (b) Matched Filter Response. (c) B/W image with vessels segmented from the software version of the algorithm. (d) Image outlining the vessels based on the segmentation result. (e) B/W image with vessels segmented from the hardware version of the algorithm. (f) Image outlining the vessels based on the segmentation result. capabilities of the system by integrating registration and pattern recognition modules. 6. Conclusion and Future Work This paper presented a high performance retinal blood vessel segmentation accelerator architecture. The architecture features a highly-parallel match filtering unit, enabling real-time blood vessel tree extraction. FPGA-based evaluation suggests that the proposed architecture outperforms existing works, demonstrating the capabilities of reconfigurable hardware for the acceleration of such algorithms. Our on-going work focuses on incorporating pre-processing and post-processing retinal processing methods into the existing system, in order to further improve accuracy. The objective is to integrate registration and recognition algorithms and their efficient implementation on reconfigurable hardware, for portable biometric and biomedical applications. 7. ACKNOWLEDGMENTS This work was co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation (Project ΝΕΑ ΥΠΟΔΟΜΗ/ΣΤΡΑΤΗ/0308/26). 8. REFERENCES [1] Usher, D. Y. Tosa, and M. Friedman, Ocular biometrics: simultaneous capture and analysis of the retina and iris, Advances in Biometrics: Sensors, Algorithms and Systems, Springer Publishers, London, UK, [2] Fraz, M.M. P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman, 2012, Blood vessel segmentation methodologies in retinal images A survey, Computer Methods and Programs in Biomedicine, Volume 108, Issue 1, [3] Perfetti, R, E. Ricci, D. Casali, G. Costantini, 2007, Cellular neural networks with virtual template expansion for retinal vessel segmentation, IEEE Trans. Circuits Syst., 54 (2), [4] Alonso-Montes, C, M Ortega, M.G. Penedo, D.L. Vilarino, 2008 Pixel parallel vessel tree extraction for a personal authentication system, Circuits and Systems,. ISCAS IEEE International Symposium on, vol., no., pp , [5] Nieto, A, V.M. Brea, D.L. Vilarino, 2009,FPGA-accelerated retinal vessel-tree extraction, Field Programmable Logic and Applications, FPL International Conference on, [6] Palomera-Perez, M.A., M.E. Martinez-Perez, H. Benitez-Perez, J.L. Ortega-Arjona, 2010, Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection, IEEE Transactions on Information Technology in Biomedicine, vol.14, no.2, [7] Muhammed Gökhan Cinsdikici, Doğan Aydın, 2009, Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm, Computer Methods and Programs in Biomedicine, Volume 96, Issue 2, November 2009, [8] Chaudhuri, S, S. Chatterjee, N. Katz, M. Nelson,; Goldbaum, M, 1989, Detection of blood vessels in retinal images using twodimensional matched filters, IEEE Transactions on Medical Imaging, 8 (3). pp ISSN [9] Raja J.B., C.G. Ravichandran, November 2001, Blood Vessel Segmentation For High Resolution Retinal Images, Inter. J. of Comp. Science Issues,vol.8, Issue 6, no 2. [10] Hijazi, M. H. A, F. Coenen, and Y. Zheng, 2010, Retinal image classification using a histogram based approach. In Proc. International Joint Conference on Neural Networks, pages IEEE.. [11] Niemeijer M., J.J. Staal, B.v. Ginneken, M. Loog, M.D. Abramoff, DRIVE: digital retinal images for vessel extraction, [12] Hoover, A.D. Kouznetsova, V. Goldbaum, M, March 2000, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging, vol.19, no.3, pp [13] MESSIDOR: Methods for Evaluating Segmentation and Indexing Techniques Dedicated to Retinal Ophthalmology, [14] Jain Anil, Lin Hong, and Sharath Pankanti Biometric identification. Commun. ACM 43, 2 (February 2000), DOI= / [15] Feng P., Y. Pan, B. Wei, W. Jin, and D. Mi, 2007, Enhancing retinal image by the contourlet transform. Pattern Recognition Letters, 4(28): [16] Kanski J.J., Clinical Opthalmology, 6th ed., Elsevier Health Sciences, London, UK, [17] Teng T., M. Lefley, D. Claremont, 2002, Progress towards automated diabetic occular screening: a review of image analysis and intelligent systems for diabetic retinopathy, Medical and Biological Engineering and Computing 40 (2002) [18] Heneghan C., J. Flynn, M. O'Keefe, M. Cahill, 2002, Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis, Medical Image Analysis 6, [19] Fowers, J., Brown, G., Cooke, P., and Stitt, G. A Performance Energy Comparison of FPGAs, GPUs, and Multicores for Sliding- Window, Applications. Proceedings of the ACM/SIGDA international symposium on Field Programmable Gate Arrays (FPGA'12), New York, NY, USA, [20] Maclean, W. J An Evaluation of the Suitability of FPGAs for Embedded Vision Systems. Proceedings of the 2005 IEEE Computer Society Conference on Computer vision and pattern recognition, San Diego,CA, 131. [21] Thisius, R.S., A, Kjaer-Nielsen, A, S, Sorensen, 2011, Real-time medical video processing, enabled by hardware accelerated correlations, Journal of Real-Time Image Processing, September 2011, Volume 6, Issue 3, pp
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Home >Food grade grease handles high temperatures
Food grade grease handles high temperatures
05 February 2018
SKF has introduced a new high temperature, harsh environment food grade grease. LGED 2 uses a PFPE oil in combination with a PTFE thickener and is registered as NSF H1.
SKF says LGED 2 has excellent oxidation resistance, extremely low evaporation losses at high temperature, good corrosion resistance, and longevity in aggressive environments.
The grease features a wide operating temperature range (-30 to +240°C). It is suitable for extreme applications,such as wafer-baking ovens in the food industry or applications exposed to vacuum or high concentrations of oxygen.
It is also appropriate in other non-food-related applications, such as oxygen valves in the steel industry or health institutions, dryers in the textile industry, and the lubrication of plastic and/or rubber surfaces.
- Condition monitoring sensor suitable for hazardous environments
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Eiríkr is a part of Project Characters and has been graded as historical.
|Approx. 77 moons (6.41 years) at death
|Cause of Death
|Old age
|ShadeClan's Founding
|Last Post
|Unknown tom
|Unknown she-cat
|Tenebris (formerly), Unnamed she-cats (formerly)
|Agnar, Brynja, Unnamed cats
|Leader Position
Eiríkr was a black smoke and white tom with ghost stripes and piercing yellow eyes. The tom was local to the territories before the Clans came into existence. He had the luck of becoming the founder of ShadeClan, a group that claimed his homeland in the thick pine forest.
The tom became a driving force in early traditions for ShadeClan, some of which are still in effect for his modern ancestors. Eiríkr was described as a merciless tom with a driving need to conquer what he didn't have. Sadly, the tom passed away from old age only days off of seeing his grandchildren.
- Eiríkr was a large cat with a build that eventually would influence the way modern ShadeClan cats are meant to look. He had a stocky build with shorter legs that were thick with muscle. This tom was a literal force to be reckoned with. He stood about half a head taller than most other toms, so he could tower over most of his rivals without really trying. The tom had a thick, medium-length pelt that by the end of his life, was tattered beyond belief. He had deep scars around his neck and belly from numerous fights throughout his life. They never healed well so the fur never grew back around the area. Eiríkr also had deep scars on his muzzle and head where it appears something larger than a cat had tried to pick him up and take him away.
- The tom had a black smoke pelt. His pelt was primarily black, especially on his back. From there it faded slightly to a lighter grey. He also had some ghost striping here and there and it should be mentioned his legs were considerably darker than any other point on his body. Eiríkr had some white spotting along his belly, muzzle and his toes. Like his previous scars, his ears were quite beaten up too. His right ear alone was almost completely gone by the end of his life and he also appeared to have rheumy eyes. His eyes were a piercing yellow and often or not his pupils were slits, making him appear quite snake-like to some.
- Now, Eiríkr, was an absolutely despicable character in short. Something along the lines of what happened to him growing up shaped the way he would act and treat others as an adult. It has alluded to that Eiríkr faced quite a challenge growing up due to the way his family was structured. He was essentially raised without proper love or care, except for the hidden affection given to him by his mother - that was, up until she was murdered by the group's former leader. Eiríkr became a pawn in a battle he had no idea about and became this cruel and calculating beast. The tom basically would follow any order he was given and execute it with precision.
- That was until he broke his 'programming'. At some stage, Eiríkr decided enough was enough. Maybe it was the haunting nightmares he began to suffer after seeing the faces of those he had thoroughly ended. So, in short, the tom snapped and eliminated his former controllers. That began a new chapter where he was now free to control his own destiny. Essentially, this was the cat many learned to respect or fear. Eiríkr became an excellent leader of sorts. He knew how to keep his cats from committing mutiny or any other crime and he also remained popular. Eiríkr managed to become quite influential amongst his own cats and is remembered for his leading style by his kin.
- Let it also be said that even after founding ShadeClan, the tom would still occasionally have to revert back to his old teachings. He wasn't afraid of resorting to violence come anything if he couldn't get his way. Eiríkr was known for his desire to conquer what he couldn't always have. He would have blood instead if that would, for the lack of better phrasing, quench the thirst he often had. After all his struggles, all he wanted was the best for he believed he deserved it.
- As previously mentioned and noted, Eiríkr was an insanely talented fighter. He was raised in an odd environment and was taught how to protect himself and how to kill. That was the basis of all the attention he received as a child (it wasn't much outside of these two things). In short, Eiríkr's fighting style relies heavily on stealth most of the time. However, if he is out in the open he has size and weight on his side to overpower his opponents. The tom was arguably what would've been called an assassin for the early part of his life - having to chase down targets and eliminate them. Following his freedom, he only used his extensive skill set to further his territory and place as ShadeClan's founding leader.
- His early life is practically unknown. Eiríkr was never able to recall his parents or their names. Actually, that remains ambiguous as he recalls the she-cat he called mother, but not her name. He was raised in a group called Helheim and one of their customs was that nursing mothers would share the task amongst themselves. The young tom was raised alongside many other kittens and still doesn't know if he had any siblings or simply none at all. In these early stages, life was comfortable for him as he and his mothers were always fed first and their needs were met instantly. But, then he was weaned and the true tale of this tom begins.
- As soon as he could eat prey, Eiríkr was taught how to hunt to provide for himself. If he failed to catch anything, then he would go hungry for the night. It was harsh at first, but, slowly he was able to keep himself going. According to the group's leaders, he showed enough progress that he started battle training. It was even harsher than going hungry for nights on end, and Eiríkr would often feel as though he would die in the claws of his mentors. However, fate would have it that he would surpass their expectations and become a mindless, killing machine just as the leaders had hoped. Eiríkr became their pawn and was used to eliminate many of the threats to their territory - including trespassers and rebels. These cats also knew how to keep him in line, as they figured out that he knew his mother and that the two were secretly keeping in contact.
- It got to the stage that Eiríkr was a legend, but, even legends can fall. As a test, they order him to kill the latest rebel - his birth mother. Unfortunately, he hesitates and upon seeing that, the leaders kill the she-cat to set the proper example. Eiríkr is then kept isolated until he is further needed. In his solitude, the tom plans a coupe and goes through with it. Alongside other cats that were forced into the same role, Eiríkr kills Helheim's leaders. However, upon conquering his controllers, he feels a power of control which makes him snap and kill some of those that aided his rebellion. Those that he keeps alive, remain by his side out of fear.
- Eiríkr and these cats claim the Helheim's former territory to create their own group. This group remains unnamed for a while until other groups begin popping up which were named Clans. As the only native, Eiríkr names his small group of cats ShadeClan to pronounce that he is the rightful owner of the lands.
- Eiríkr's past following the formation of ShadeClan, is continued in ShadeClan's History. There he becomes a legend in the eyes of his Clan in the years to come. Due to his unusual and harsh life, Eiríkr gave ShadeClan a rich history full of customs and traditions that are still seen in modern ShadeClan today. Notably, he fathers Agnar with his former mate, Tenebris, who succeeds him following his death, and is the beginning of a long family lineage that continues to lead today as well.
- Tenebris (formerly): Deceased; Dark Forest resident
- Unnamed she-cats (formerly): Deceased; Residences unknown
- Agnar: Deceased; Residence unknown
- Brynja: Deceased; Residence unknown
- Unnamed cats: Deceased; Residences unknown
- Unnamed cats: Deceased; Residences unknown
Great Granddaughter:
- Shadowstar: Deceased; StarClan resident
Great Grandchildren:
- Unnamed cats: Deceased; Residences unknown
Great-Great Grandson:
- Pheasanttail: Living
Great-Great Granddaughters:
- Spiderstar: Living
- Blackpaw: Deceased; StarClan resident
- Coming Soon
- Coming Soon
Love Interests
Unnamed she-cats
- Now, to put it quite simply, Eiríkr had quite the harem of lovers. The tom wasn't exactly a womanizer, no he couldn't really care about them. Instead, he was much more focused on ensuring he had kin to take over his rule. This ideology was the beginning of a tradition for his then young Clan. Sure, the tom might have had favoured partners due to their success as mothers or simply for their looks. But, they all remained as 'pawns' to him so that he may continue to conquer even in death. That was what Eiríkr was about.
- Coming Soon
- The name Eiríkr is an Old Norse name meaning 'ruler of the people'.
Life Image
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The Nashville Experience Visits Edley's!
The great people at The Nashville Experience dropped by Edley's this week to say hello to The Tuck Special and our signature Bushwacker while hanging out on the friendliest porch in the 12South Neighborhood. Lucky for us they were nice enough to bring along their camera!
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Current Work – Opportunities to Give Input
Following our March 29th meeting, we would like everyone to have the opportunity to give input on our current work. Please review the meeting notes, slides, and recording from the meeting before answering the below questions.
You may provide any input you like by emailing us your thoughts at firstname.lastname@example.com.
Roundtable Name – help us choose the final option:
- Roundtable for Responsible Leather (RLR), or
- Global Roundtable for Responsible Leather (GRRL)
Roundtable Mandate & Structure Task Group Work (Outcomes from Meeting #1)
Mission (choose one)
- Ensure responsibility and continuous improvement in the global leather value chain through leadership, science, advocacy, and inclusive multi-stakeholder engagement and collaboration.
- Ensure a more responsible and sustainable global leather value chain through continuous improvement, leadership, science, advocacy, and inclusive multi-stakeholder engagement and collaboration.
Vision (choose one)
- We envision a world in which all aspects of the leather value chain are environmentally and socially responsible, with a focus on animal welfare, while being economically viable.
- We envision a world in which all aspects of the leather value chain are environmentally and socially responsible, ensures the welfare of animals, and is economically viable.
***We need input on the term "ensure", other options include "support", "encourage", "promote"
Main Goals – The Roundtable will be: (Give input on the below points)
- A platform for stakeholders in the leather industry to engage with each other, share information, and identify common challenges and opportunities.
- A common voice that represents the interests of the leather value chain.
- A structure for interacting with other organizations and government bodies.
- A forum to agree on priority issues and desired solutions to advance our mission and vision.
Main Activities – These may include, but will not be restricted to: (Give input on the below point – more to come later)
- Combine the knowledge and expertise of all stakeholders to map out existing projects and initiatives, and compliment/add value to them by putting them into a common framework that the leather industry can use.
Scopes Prioritization Task Group Work (group has not started yet)
To look at which issues we should try to address through the verification system, and which we can address through separate roundtable activities. We will identify the "why" behind each issue, so that we can clearly articulate the importance of each verification scope. We can use the results from the last survey, consider a new one, and look at what is most practical to ensure we have success. We can also discuss whether there should be multiple tiers allowed (eg: bronze, silver, gold).
Refer to slides #35-38 of March 29th meeting
What should we take into consideration when setting the priorities?
- Cost and complexity of implementation
- Scalability
- Urgency of the issue
- Low-hanging fruit
- Relevance to brands to drive commitment
- Existing initiative and programs we can leverage
- Other?
You may provide any input you like by emailing us your thoughts at firstname.lastname@example.com.


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- 2018
- 2017
- 2016
- 2015
- 2014
- 2013
- 2011
- Family Dentistry (1)
- Fayetteville (1)
- Senoia (1)
- Peachtree City (1)
- Dental Implants (4)
- Dentistry (1)
- Implant Dentistry (1)
- Fruit and Teeth (1)
- Teeth Cleaning (1)
- Invisalign (4)
- Zoom! Teeth Whitening (1)
- restorative dentistry (2)
- Dental Bonding (1)
- dental crowns (1)
- dental bridges (1)
- braces (1)
- orthodontics (1)
- Oral Hygiene (1)
- family dentist (1)
- Root Canal (1)
Avella Family and Implant Dentistry
Peachtree City, GA 30269
Call For Financing Options
Avella Family & Cosmetic Dentistry
Need a root canal? A root canal is a procedure used to fix a tooth that is damaged or badly decayed. Root canal therapy will save your natural tooth and allow you to keep it functional for decades. Avella Family and Implant Dentistry, which is located in Peachtree City, GA, and serving Senoia and Fayetteville, GA, offers a full range of dental services. Dr. Peter Harris is one of the finest dentists in Peachtree City, GA and serving Senoia and Fayetteville, GA. Here are five signs you may need a root canal.
1. You have a toothache.
Tooth pain can be a sign of an infected tooth in need of root canal treatment. Its level may range from slight to extreme. A root canal will eliminate that pain. A root canal is no more painful than getting a dental filling. The discomfort experienced in the weeks or months before treatment is truly painful, not the procedure itself.
2. Cold and hot bother you.
Prolonged sensitivity to cold or hot temperatures is a sign of a tooth in need of root canal treatment. You may experience a sharp pain when the tooth is exposed to cold or hot temperatures through foods and beverages- or even by the air. Your tooth will no longer be sensitive to cold or hot temperatures after treatment.
3. Your gums are swollen.
Gum inflammation is a sign you may need root canal therapy. Tenderness or swelling is often associated with infection, although it doesn't always mean an infection is present. If your gums become swollen, they may bulge out, or protrude. Gum inflammation is a common problem and can have a number of different causes. If you are suffering from gum swelling that lasts longer than a few days, you should contact your dentist.
4. You have a bump on your gum.
A pimple-like bump on your gum is a sign that you may need a root canal. It will feel tender and can cause swelling in the area, and you may notice a bad taste in your mouth. This bump may go away and then come back. It is usually an accumulation of pus, which can be linked to infection.
5. You have a discolored tooth.
One darkened tooth is a sign that you may need a root canal. Discoloration is a sign that the nerve of the tooth is damaged. The tooth may become black, gray, dark yellow. Tooth discoloration is also related to damaged dental fillings, severe decay, or trauma so visiting your dentist for a checkup is recommended.
If you need a root canal, why wait? We can help you today! Call Avella Family and Implant Dentistry at (770) 486-1984 today to schedule an appointment in Peachtree City, GA and serving Senoia and Fayetteville, GA. You have nothing to lose except your toothache!
Get convenient full-service dental care for your whole family in Fayetteville, GA.
We know that life isn't always as simple as it should be. Whether your child refused to go to bed on time or the family pet made a mess in the house, there are problems that can make life anything but easy; however, our Peachtree City, GA, family dentist Dr. Peter Harris thinks that getting dental care for the whole family should be easy. Find out why you should consider turning to a family dentist for all your dental needs.
Trustworthy Medical Care
Bringing your child in to visit our Fayetteville dentist early on not only helps establish rapport with us early on but it also takes the fear out of dental care. We know that there are a lot of adults out there that fear the dentist. By having children come in regularly when they are very young, this establishes rapport and trust between your little one and our dentist, making these six-month cleanings a breeze in the future.
Reliable Treatment
We get to really know your family over the years. From the very beginning, we take the time we need to be able to understand your detailed family history and preexisting conditions so that we can provide the proper care and services that are right for you. Since we understand your medical history and risk factors we know who is at risk for gum disease or other dental problems so that we can provide comprehensive care and treatment to prevent issues altogether.
Convenience for the Whole Family
Nothing is worse than having to run around any more than you have to, right? We think so, too. Life is already hectic enough so why go to different dentists when your whole family can get the care they need right here in our office. That's right. From routine cleanings and Invisalign to children's orthodontic treatment and dentures, we offer it all to cater to your ever-changing needs. Now isn't that a relief?
Do you have questions about how to care for your child's smile? Need to schedule dental cleanings for the whole family? Then call Avella Family & Cosmetic Dentistry in Peachtree City, GA, and serving Fayetteville, GA today.
Spending just a few minutes a day on oral hygiene offers important protections for your teeth. Fayetteville, GA, dentist Dr. Peter Harris explains how four oral hygiene mainstays can help you keep your smile healthy.
Twice-daily brushing reduces your cavity risk
Brushing is a simple, yet very effective, way to remove plaque, the bacterial film that causes cavities. Brushing in the morning and evening not only gets rid of plaque but also loosens food particles stuck to your teeth and helps improve the freshness of your breath.
Most toothpastes available in drugstores contain fluoride, a mineral that strengthens weak areas of tooth enamel. When you remember to brush two times a day with fluoridated toothpaste, you'll keep your teeth strong and lower your cavity risk. Regular brushing also prevents plaque from turning into tartar, a hard deposit that is responsible for periodontal disease.
A gentle touch is best when you brush your teeth. If you brush too hard or don't use a brush with soft bristles, you may damage your gums and expose your tooth roots. For best results, devote at least two minutes to your toothbrushing routine.
Flossing ensures you don't miss a spot
If you only brush your teeth and don't floss, plaque can still remain on the areas between your teeth. Luckily, dental floss is thin enough to fit into those tight spaces. The gentle up-and-down motion you use when you floss your teeth dislodges both plaque and food debris.
Mouthwash offers added benefits
Anti-cavity mouthwashes kill bacteria that can cause cavities and bad breath. Do you rinse your mouth after using mouthwash? Rinsing washes away the protection that mouthwash provides. Instead of rinsing, spit out the mouthwash after you swish it around in your mouth and wait at least 30 minutes before drinking water or another beverage.
Regular dental visits help you maintain excellent dental health
Visiting our Fayetteville office twice a year is an excellent way to protect your teeth. During your visit, we'll remove plaque and tartar from your teeth and examine your mouth for signs of decay, gum disease, damage to fillings and restorations, signs of infections and other issues that can affect your teeth, mouth, jaw and gums.
Keep your smile bright and healthy with good oral hygiene and regular dental visits. Call Fayetteville, GA, dentist Dr. Peter Harris at (770) 486-1984 to schedule your visit.
Do you feel as though it will take a miracle to give your smile a second chance due to your missing teeth and gaps? If so, you could benefit from dental implants. This important, permanent, and effective tooth replacement option can overhaul your smile whether you are missing one, several, or all of your teeth. Find out more about dental implants and what they can do for you with Dr. Peter Harris at Avella Family and Cosmetic Dentistry in Peachtree City, GA.
How do dental implants work?
Dental implants have three main parts. The fixture is a titanium post which your dentist implants into the jawbone underneath your gum tissue. Over the healing process, the bone grows around the fixture to hold it permanently in place, a process called osseointegration. The implant's abutment connects the fixture to the prosthetic tooth. The prosthetic, normally a dental crown or bridge, replaces the tooth or teeth.
Can dental implants help me?
Dental implants' versatility allows dentists to use them in many situations. Some of the most common uses for dental implants include:
- Single Tooth Replacement: Used in replacing a single tooth, this method uses one dental implant to provide the foundation for one prosthetic tooth.
- Multiple Tooth Replacement: Patients who have a gap which spans several teeth in a row can replace their teeth using a dental bridge held in place by two fixtures on either side of the restoration. Unlike a traditional dental bridge, the implants stand alone, keeping the surrounding, healthy teeth intact.
- Implant-Supported Dentures: Patients missing all of the teeth on an arch can permanently replace their teeth using implant-supported dentures. Luckily, the denture does not require a single implant for each tooth. Rather, four or more implants throughout the mouth hold a full denture in place.
Dental Implants in Peachtree City, GA
A good candidate for dental implants should be in great physical and dental health, with no signs of tooth decay or gum disease. Since the implants' fixtures rely on the jawbone to hold them in place, patients should have minimal bone atrophy in the areas of the implants. Patients with atrophy may require a bone graft prior to placing their implants.
For more information on dental implants, please contact Dr. Peter Harris at Avella Family and Cosmetic Dentistry in Peachtree City, GA. Call 770-486-1984 to schedule your appointment with Dr. Harris today!
If you avoid pictures due to not liking your teeth, you could benefit from Invisalign orthodontic treatment. This technique will overhaul your smile, correcting your bite and straightening your teeth to give you a whole new look. Say, "Cheese!" with confidence and find out more about Invisalign treatment with Dr. Peter Harris at Avella Family and Cosmetic Dentistry, located in Peachtree City, GA, and serving the Senoia and Fayetteville, GA.
Do I need orthodontic treatment?
Talking with your dentist is the best and easiest way to determine if you would benefit from orthodontic treatment. However, there are some questions that can help you get to that point. Do you feel as though your teeth are crooked and weigh down your appearance? Do you notice that your top teeth lie too far in front of or behind your bottom teeth? Do your teeth open where they meet? Does your jaw hurt or ache after chewing or eating? If you answered "yes" to any of these questions, you should ask your dentist about orthodontic treatment.
Is Invisalign right for me and my smile?
Invisalign can benefit anyone who requires orthodontic treatment. In some rare cases, patients with severe misalignments or bite issues may not qualify for Invisalign and will need to wear traditional braces instead. However, most patients can straighten their smile and improve their bite using Invisalign. A quick consultation with your dentist can help you understand which treatment is best for you.
Invisalign Treatment in Peachtree City, Senoia and Fayetteville, GA
Invisalign uses clear aligner trays rather than the metal brackets and wires associated with traditional braces. This means that you can straighten your teeth without compromising your look with metal permanently attached to your teeth. Additionally, Invisalign patients simply remove their aligner trays to eat and brush their teeth, and there's no need to avoid certain foods you may love. During treatment, patients wear each aligner tray in their series for about two weeks before switching it for the next tray. Most Invisalign treatments average about a year in length though treatment time can vary from patient to patient.
For more information on Invisalign treatment, please contact Dr. Harris at Avella Family and Cosmetic Dentistry, serving Peachtree City, Senoia and Fayetteville, located in Peachtree City, GA. Call (770) 486-1984 to schedule your appointment with Dr. Harris today!
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Welcome to Ketogenic Recipes! Enjoy easy keto recipes and our best keto meal ideas. Navigate with the three bar icon in the left corner.
Use high fiber tortillas for keto pizza crusts
Fluffy chocolate mousse for keto desserts
Featuring cream cheese & dill omelet
Keto chili rellenos with mushrooms
Featuring mint chocolate chip
My favorite keto shake recipe!
Delicious low carb smoothies
Creamy & delicious
Loaded with vitamin C for immunity
Learn how to get started on the keto diet
Advice for losing weight on the keto diet
Best practices from our personal experiences


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Bacterial & Fungal
Bacterial & Fungal
API Pimafix
Pimafix rapidly treats fungus or cottony growth, mouth and body fungus and reddening of the fins and body. Pimafix also treats internal and external bacterial infections.
Prices start from: $18.10
API Turtle Fix 118ml
A safe all natural way to treat topical bacteria infections on the body and limbs of turtles, frogs and other aquatic/semi-aquatic reptiles and amphibians.
Price: $13.35
Aquasonic Methylene Blue 250ml
Aids in the treatment of white spot disease and superficial fungal infections. Safe for freshwater and saltwater aquariums. Also prevents fungal infection on fish eggs.
Price: $17.50
Blue Planet Aquari-Cycline Tablets
Broad spectrum antibiotic for the treatment of diseases in ornamental fresh and saltwater aquarium fish caused by teracycline sensitive bacteria.
Prices start from: $26.70
Wardley Fungus-Ade
Aquaria formulation treatment aid for fungal infections, fin and tail rot.
Prices start from: $9.65
Waterlife Myxazin
To treat fin rot, body rot, ulcers, sores and other bacterial infections. Myxazin will also help to control pop eye disease, cloudy eyes and mouth fungus.
Prices start from: $29.10
API BettaFix 50ml
Use whenever Bettas exhibit signs of bacterial or fungal disease, such as red ulcers, frayed or torn fins, slimy patches or cottony growths.
Price: $13.25
Showing 1 — 7 of 7