An Automatic Indirect Immunofluorescence Cell Segmentation System
Author(s) -
YungKuan Chan,
DerChen Huang,
KuoChing Liu,
RongTai Chen,
Xiaoyi Jiang
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/501206
Subject(s) - iif , immunofluorescence , autoantibody , indirect immunofluorescence , population , segmentation , artificial intelligence , fluorescence microscope , pattern recognition (psychology) , fluorescence , computer vision , computer science , biology , immunology , antibody , medicine , physics , optics , environmental health
Indirect immunofluorescence (IIF) with HEp-2 cells has been used for the detection of antinuclear autoantibodies (ANA) in systemic autoimmune diseases. The ANA testing allows us to scan a broad range of autoantibody entities and to describe them by distinct fluorescence patterns. Automatic inspection for fluorescence patterns in an IIF image can assist physicians, without relevant experience, in making correct diagnosis. How to segment the cells from an IIF image is essential in developing an automatic inspection system for ANA testing. This paper focuses on the cell detection and segmentation; an efficient method is proposed for automatically detecting the cells with fluorescence pattern in an IIF image. Cell culture is a process in which cells grow under control. Cell counting technology plays an important role in measuring the cell density in a culture tank. Moreover, assessing medium suitability, determining population doubling times, and monitoring cell growth in cultures all require a means of quantifying cell population. The proposed method also can be used to count the cells from an image taken under a fluorescence microscope
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