Oriented active shape models
2006
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.
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