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Digital Image Processing Algorithms for Cell Centroid and Boundary Detection in Two Dimensions and Cellular Network Mapp

Current progress and goals. When using the gray-space method on real-life cellular images to single out nerve cell centroids, the algorithm often misses a few pixels here and there from the cell processes, and sometimes, when identifying the background, the region ?bleeds" into areas that are actual

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Digital Image Processing Algorithms for Cell Centroid and Boundary Detection in Two Dimensions and Cellular Network Mapp

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    1. Digital Image Processing Algorithms for Cell Centroid and Boundary Detection in Two Dimensions and Cellular Network Mapping in the Time Domain Stephen Kanji Chen Hsinchu, Taiwan Tuesday, August 15 2006

    2. Current progress and goals When using the gray-space method on real-life cellular images to single out nerve cell centroids, the algorithm often misses a few pixels here and there from the cell processes, and sometimes, when identifying the background, the region “bleeds” into areas that are actual cell centroids. There are two simple methods to counter this: Allow the option for a two-pass filter. The algorithm outputs a false-color meta-image that color codes different regions: the background, cell bodies, and cell boundaries. Running the algorithm again on the meta-image will eliminate nearly 100% of these pixelation errors. Add a few lines of code to allow for color bias during the grayscale conversion process. Since cells are often dyed a particular color, the conversion needs to optimize for that particular color so that no contrast information is lost during the conversion process. Some of the issues with region-bleeding is due to poor contrast. Test the current image processing algorithms we have created (boundary detection, cell centroid detection, image histogram thresholding, gray-space segmentation) on actual images and motion pictures from Dr. Silva’s lab in San Diego.

    3. Goals (continued) We have decided that due to time constraints and practical reasons, it is not worth implementing cell network detection in static images. Analysis of high-quality movie clips is much more efficient and accurate. In this respect, we will begin working on a time-domain analysis of images next week. Chances are that a week will only give us enough time to implement a simple algorithm that has the basic functionality of a demonstration, but as we continue to pursue this line of research during the upcoming academic year, focusing on developing an accurate and robust cell network mapping algorithm will be the primary objective.

    4. Photos of the week T minus 48 seconds I have yet to understand the reason why many intersections in the city have these countdown timers. If things like these were installed in the US for the drivers to see, I bet there would be a lot more incidents of street racing…!

    5. Photos of the week

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