NDLI: Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization

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Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization

Content Provider	PubMed Central
Author	Liao, Shu Gao, Yaozong Shi, Yinghuan Yousuf, Ambereen Karademir, Ibrahim Oto, Aytekin Shen, Dinggang
Copyright Year	2013
Abstract	Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.
Starting Page	511
File Format	PDF
ISSN	10112499
Journal	Information processing in medical imaging : proceedings of the ... conference
Volume Number	23
Language	English
Publisher Date	2013-01-01
Access Restriction	Open
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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