NDLI: A distribution-matching approach to MRI brain tumor segmentation

Content Provider	IEEE Xplore Digital Library
Author	Njeh, I. Ben Ayed, I. Ben Hamida, A.
Copyright Year	2012
Description	Author affiliation: ATMS Advanced Technologies for Medecine and Signals, ENIS, Sfax University, Tunisia (Njeh, I.; Ben Hamida, A.) \|\| GE Healthcare, London, ON, Canada (Ben Ayed, I.)
Abstract	This study investigates a fast distribution-matching algorithm for brain tumor segmentation. From a very simple user input, we learn a non-parametric model distribution which contains all the statistical information about the normal regions in the current brain image. We state the problem as the optimization of a cost function containing (1) an intensity distribution matching prior which measures a global similarity between non-parametric distributions, and (2) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optimum of the cost function yields the complement of the tumor region in nearly real-time. Based on global rather than pixelwise information, the proposed algorithm does not require a complex learning from a large training set, as is the case in existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations and comparisons with several existing methods over publicly available data demonstrate that the proposed algorithm can yield a competitive performance.
Starting Page	1707
Ending Page	1710
File Size	224895
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Image segmentation Delta modulation Training Brain modeling Cost function graph cuts brain tumors MRI distribution matching bound relaxation
Content Type	Text
Resource Type	Article

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