NDLI: MR prostate segmentation via distributed discriminative dictionary (DDD) learning

Content Provider	IEEE Xplore Digital Library
Author	Yanrong Guo Yiqiang Zhan Yaozong Gao Jianguo Jiang Dinggang Shen
Copyright Year	2013
Description	Author affiliation: Siemens Healthcare, Malvern, PA, USA (Yiqiang Zhan) \|\| Dept. of Radiol., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Yaozong Gao; Dinggang Shen) \|\| Sch. of Comput. & Inf., Hefei Univ. of Technol., Hefei, China (Yanrong Guo; Jianguo Jiang)
Abstract	Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary (DDD) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First, minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second, linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third, instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.
Sponsorship	IEEE
Starting Page	868
Ending Page	871
File Size	942240
Page Count	4
File Format	PDF
ISBN	9781467364560
ISSN	19457928
e-ISBN	9781467364553
DOI	10.1109/ISBI.2013.6556613
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Image segmentation Shape Active appearance model Standards Deformable models Silicon deformable segmentation Prostate segmentation magnetic resonance image sparse dictionary learning
Content Type	Text
Resource Type	Article

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