Segmenting cell images

A deterministic relaxation approach

Chee Sun Won, Jae Yeal Nam, Yoonsik Choe

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)281-291
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3117
Publication statusPublished - 2004 Dec 1

Fingerprint

Blood
Red Blood Cells
Segmentation
Cell
Image segmentation
Nucleus
Labels
Cells
Adaptive Thresholding
Maximum a Posteriori
Image Segmentation
Histogram
Update
Line
Background

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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