Oriented Active Shape Models

• Published in: IEEE transactions on medical imaging Vol. 28; no. 4; pp. 571 - 584
• Main Authors: Jiamin Liu, Udupa, J.K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Active shape model; Active shape models (ASMs); Algorithms; Biomedical imaging; Breast; Breast - anatomy & histology; Cervical Vertebrae - anatomy & histology; Computed tomography; Dynamic programming; Foot - anatomy & histology; Humans; Image recognition; Image segmentation; image-based segmentation; Imaging, Three-Dimensional - methods; live wire; Liver - anatomy & histology; Magnetic resonance; Magnetic Resonance Imaging; model-based segmentation; Models, Biological; Multivariate Analysis; Spine; Studies; Tomography, X-Ray Computed; Wire
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2008.2007820

Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. 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 characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results 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 and initialization becomes automatic. 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 found by the 2-level dynamic programming algorithm.