NDLI: Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF

Content Provider	IEEE Xplore Digital Library
Author	Ismail, M. Mostapha, M. Soliman, A. Nitzken, M. Khalifa, F. Elnakib, A. Gimel'farb, G. Casanova, M.F. El-Baz, A.
Copyright Year	2015
Description	Author affiliation: Dept. of of Psychiatry & Behavioral Sci., Univ. of Louisville, Louisville, KY, USA (Casanova, M.F.) \|\| Bioeng. Dept.., Univ. of Louisville, Louisville, KY, USA (Ismail, M.; Mostapha, M.; Soliman, A.; Nitzken, M.; Khalifa, F.; Elnakib, A.; El-Baz, A.) \|\| Dept. of Comput. Sci., Univ. of Auckland, Auckland, New Zealand (Gimel'farb, G.)
Abstract	This paper introduces a new framework for the segmentation of different brain structures from 3D infant MR brain images. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on higher-order visual appearance characteristics of infant MRIs. These characteristics are described using voxel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of infant brain signals, a Linear Combination of Discrete Gaussians (LCDG) is used that has positive and negative components. Also to accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth-order families with a traditional second-order model is proposed. The proposed approach was tested on 40 in-vivo infant 3D MR brain scans, having their ground truth created by an expert radiologist, using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results promise an accurate segmentation of infant MR brain images compared to current open source segmentation tools.
Starting Page	4327
Ending Page	4331
File Size	1943732
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351623
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Shape Image segmentation Three-dimensional displays Magnetic resonance imaging Databases Infant Brain Segmentation Adaptive Shape Higher Order MGRF
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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