Abstract
Automatic segmentation of digital cell images into four regions, namely nucleus, cytoplasm, red blood cell (rbc), and background, is an important step for pathological measurements. Using an adaptive thresholding of the histogram, the cell image can be roughly segmented into three regions: nucleus, a mixture of cytoplasm and rbc’s, and background. This segmentation is served as an initial segmentation for our iterative image segmentation algorithm. Specifically, MAP (maximum a posteriori) criterion formulated by the Bayesian framework with the original image data and local variance image field (LVIF) is used to update the class labels iteratively by a deterministic relaxation algorithm. Finally, we draw a line to separate the touching rbc from the cytoplasm.
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© 2004 Springer-Verlag Berlin Heidelberg
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Won, C.S., Nam, J.Y., Choe, Y. (2004). Segmenting Cell Images: A Deterministic Relaxation Approach. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_24
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DOI: https://doi.org/10.1007/978-3-540-27816-0_24
Publisher Name: Springer, Berlin, Heidelberg
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