Automated sub-cellular phenotype classification: an introduction and recent results
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Journal of Biomedical Informatics
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Geo-thresholding for segmentation of fluorescent microscopic cell images
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Towards automated cellular image segmentation for RNAi genome-wide screening
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Motivation: 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. Results: We present an effective implementation of the fuzzy c-means algorithm for extracting puncta (spots), representing fluorescent-labeled peroxisomes, which are subject to low contrast. We make use of the quadtree partition to enhance the fuzzy c-means based segmentation and to disregard regions which contain no target objects (peroxisomes) in order to minimize considerable time taken by the iterative process of the fuzzy c-means algorithm. We finally isolate touching peroxisomes by an aspect-ratio criterion. The proposed approach has been applied to extract peroxisomes contained in several sets of color images and the results are superior to those obtained from a number of standard techniques for spot extraction. Availability: Image data and computer codes written in Matlab are available upon request from the first author.