Abstract
We have developed a method to rapidly test the quality of a biological image, to identify appropriate segmentation methods that will render high quality segmentations for cells within that image. The key contribution is the development of a measure of the clarity of an individual biological cell within an image that can be quickly and directly used to select a segmentation method during a high content screening process. This method is based on the gradient of the pixel intensity field at cell edges and on the distribution of pixel intensities just inside cell edges. We have developed a technique to synthesize biological cell images with varying qualities to create standardized images for testing segmentation methods. Differences in quality indices reflect observed differences in resulting masks of the same cell imaged under a variety of conditions.
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Peskin, A.P., Kafadar, K., Dima, A. (2009). A Quality Pre-processor for Biological Cell Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_101
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DOI: https://doi.org/10.1007/978-3-642-10520-3_101
Publisher Name: Springer, Berlin, Heidelberg
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