NDLI: Medical image segmentation based on non-parametric mixture models with spatial information

Content Provider	Springer Nature Link
Author	Song, Yu Qing Liu, Zhe Chen, Jian Mei Zhu, Feng Xie, Cong Hua
Copyright Year	2010
Abstract	Because of too much dependence on prior assumptions, parametric estimation methods using finite mixture models are sensitive to noise in image segmentation. In this study, we developed a new medical image segmentation method based on non-parametric mixture models with spatial information. First, we designed the non-parametric image mixture models based on the cosine orthogonal sequence and defined the spatial information functions to obtain the spatial neighborhood information. Second, we calculated the orthogonal polynomial coefficients and the mixing ratio of the models using expectation-maximization (EM) algorithm, to classify the images by Bayesian Principle. This method can effectively overcome the problem of model mismatch, restrain noise, and keep the edge property well. In comparison with other methods, our method appears to have a better performance in the segmentation of simulated brain images and computed tomography (CT) images.
Starting Page	569
Ending Page	578
Page Count	10
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	6
Issue Number	4
e-ISSN	18631711
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-10-10
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Non-parametric mixture models Smoothing parameter Expectation-maximization cosine orthogonal sequence Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Multimedia Information Systems Signal, Image and Speech Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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