NDLI: Efficient sparse shape composition with its applications in biomedical image analysis: An overview

Content Provider	IEEE Xplore Digital Library
Author	Shaoting Zhang Yiqiang Zhan Yan Zhou Metaxas, D.N.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ, USA (Shaoting Zhang; Metaxas, D.N.) \|\| CAD R&D, Siemens Healthcare, Malvern, PA USA (Yiqiang Zhan) \|\| CMS Software, Elekta Inc, MO, USA (Yan Zhou)
Abstract	Shape information plays an important role in biomedical image analysis because of the strong shape characteristics of biological structures. It is often used as a prior to constrain or refine the intermediate shape information derived from low-level image features. In this paper, we give an overview of the sparse shape composition based prior modeling method and its various applications of biomedical image analysis. Instead of learning a generative shape model, it incorporates shape priors on-the-fly through the sparse shape composition. Particularly, a shape instance derived from low level image features is refined by a sparse linear combination of a sparse set of shapes in the repository. We also design three strategies to improve run-time efficiency. 1) When the shape repository contains a large number of instances, K-SVD can be used to learn a more compact but still informative shape dictionary. 2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, affinity propagation method can be used to partition the surface into small subregions. 3) When there are multiple structures, hierarchical scheme is employed to model them simultaneously. These strategies decrease the scale of the sparse optimization problem and thus speed up the algorithm. Our method is applied on different biomedical image analysis problems, including localization, tracking and segmentation of anatomical structures. In all of these applications, this method achieves promising results.
Starting Page	976
Ending Page	979
File Size	345092
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235720
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	Shape Image segmentation Dictionaries Biomedical imaging Computational modeling Lungs Rodents tracking Shape prior sparse representation dictionary learning segmentation
Content Type	Text
Resource Type	Article

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