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.
|Number of pages||11|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2004 Dec 1|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)