Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification

In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In this paper, we present an unsupervised Bayesian classification scheme for separatin...

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Bibliographic Details
Published in:	IEEE transactions on biomedical engineering Vol. 57; no. 12; pp. 2825 - 2832
Main Authors:	Jung, Chanho, Kim, Changick, Chae, Seoung Wan, Oh, Sukjoong
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01-12-2010 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Automatic cell segmentation Bayes Theorem Bayesian methods Biological and medical sciences Biomedical imaging Breast - cytology Breast Neoplasms Carcinoma, Ductal, Breast Cell Nucleus - ultrastructure Cervix Uteri - cytology Cluster Analysis cluster validation Clustering algorithms Discriminant Analysis Female Gaussian mixture model Gaussian processes Histocytochemistry - methods Hospitals Humans Image analysis Image Processing, Computer-Assisted - methods Image segmentation Investigative techniques, diagnostic techniques (general aspects) Medical diagnostic imaging Medical sciences Miscellaneous. Technology Normal Distribution overlapped nuclei segmentation Pathology Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques Reproducibility of Results Surface topography unsupervised Bayesian classifier Segmentation Image processing Cell nucleus Automatic cell segmentation cluster validation Mixture theory overlapped nuclei segmentation Overlap Unsupervised classification Gaussian mixture model Gaussian process Microscopy unsupervised Bayesian classifier Cell Biomedical engineering
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