Oriented active shape models

Jiamin Liu; Jayaram K. Udupa

doi:10.1117/12.653589

10 March 2006 Oriented active shape models

Jiamin Liu, Jayaram K. Udupa

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 614404 (2006) https://doi.org/10.1117/12.653589
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

Active Shape Models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, we present a novel strategy called Oriented Active Shape Models (OASM) in an attempt to overcome the following three major limitations of ASM: (1) poor delineation accuracy, (2) the requirement of a large number of landmarks, (3) the problem of sensitivity to search range to recognize the object boundary. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter allow live wire to effectively separate an object boundary from other non object boundaries with similar properties that come very close in the image domain. Our approach leads us to a 2-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation. Our experiments in segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine in MR and CT images indicate the following: (1) The accuracy of segmentation via OASM is considerably better than that of ASM. (2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM. (3) OASM becomes largely independent of search range. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution of dynamic programming.

Citation Download Citation

Jiamin Liu and Jayaram K. Udupa "Oriented active shape models", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614404 (10 March 2006); https://doi.org/10.1117/12.653589

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