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Extraction of fluorescent cell puncta by adaptive fuzzy segmentation
Author(s) -
Tuan D. Pham,
Denis I. Crane,
Tuan H. Tran,
Tam H. Nguyen
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth213
Subject(s) - segmentation , fuzzy logic , computer science , artificial intelligence , extraction (chemistry) , fluorescence , pattern recognition (psychology) , computer vision , computational biology , biology , chemistry , chromatography , physics , quantum mechanics
The discrimination and measurement of fluorescent-labeled vesicles using microscopic analysis of fixed cells presents a challenge for biologists interested in quantifying the abundance, size and distribution of such vesicles in normal and abnormal cellular situations. In the specific application reported here, we were interested in quantifying changes to the population of a major organelle, the peroxisome, in cells from normal control patients and from patients with a defect in peroxisome biogenesis. In the latter, peroxisomes are present as larger vesicular structures with a more restricted cytoplasmic distribution. Existing image processing methods for extracting fluorescent cell puncta do not provide useful results and therefore, there is a need to develop some new approaches for dealing with such a task effectively.

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