NDLI: Stochastic Model for Medical Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Barzily, Z. Mingyue Ding Volkovich, Z.
Copyright Year	2014
Description	Author affiliation: Software Eng. Dept., ORT Braude Coll., Karmiel, Israel (Barzily, Z.; Volkovich, Z.) \|\| Bio-Med. Eng. Dept., Huazhong Univ. of Sci. & Technol., Wuhan, China (Mingyue Ding)
Abstract	Stochastic modeling in image analysis aims to represent the images features in a small number of parameters so as to recognize the source producing the images. In this paper we address the image segmentation problem in the case of significantly differ segments' sizes. A probabilistic model dealing the distribution of gray level in the observed image is based on the Gaussian Mixture Model identifying each component a segment. According to the general segmentation methodology for multi-modal gray levels images we presume that every region-of-interest attaches to a distinct substantial mode of the empirical distribution of gray levels. So, the number of the components is evaluated via a new resampling procedure involving the Expectation-Maximization algorithm used in order to estimate the significant histograms picks. Stable states of our model are associated within of the proposed method with the "true" segments quantities specified by the appropriate components' quantities. Numerical experiments demonstrate the high ability of the proposed method.
Starting Page	362
Ending Page	369
File Size	440754
Page Count	8
File Format	PDF
ISBN	9781479942237
DOI	10.1109/ARES.2014.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-08
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Image segmentation Histograms Clustering algorithms EM clustering Approximation algorithms Approximation methods mage segmentation stochastic model
Content Type	Text
Resource Type	Article

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