Literature DB >> 19336277

Oriented active shape models.

Jiamin Liu1, Jayaram K Udupa.   

Abstract

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.

Mesh:

Year:  2009        PMID: 19336277     DOI: 10.1109/TMI.2008.2007820

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  12 in total

1.  Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.

Authors:  Yaozong Gao; Yeqin Shao; Jun Lian; Andrew Z Wang; Ronald C Chen; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2016-01-18       Impact factor: 10.048

2.  Medical image segmentation by combining graph cuts and oriented active appearance models.

Authors:  Xinjian Chen; Jayaram K Udupa; Ulas Bagci; Ying Zhuge; Jianhua Yao
Journal:  IEEE Trans Image Process       Date:  2012-01-31       Impact factor: 10.856

3.  Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

Authors:  Krzysztof Chris Ciesielski; Paulo A V Miranda; Alexandre X Falcão; Jayaram K Udupa
Journal:  Med Image Anal       Date:  2013-07-03       Impact factor: 8.545

4.  Automatic anatomy recognition via multiobject oriented active shape models.

Authors:  Xinjian Chen; Jayaram K Udupa; Abass Alavi; Drew A Torigian
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

5.  3D automatic anatomy segmentation based on iterative graph-cut-ASM.

Authors:  Xinjian Chen; Ulas Bagci
Journal:  Med Phys       Date:  2011-08       Impact factor: 4.071

6.  Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images.

Authors:  Shandong Wu; Susan P Weinstein; Emily F Conant; Mitchell D Schnall; Despina Kontos
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

7.  HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.

Authors:  Qingzhu Wang; Wanjun Kang; Haihui Hu; Bin Wang
Journal:  J Med Syst       Date:  2016-06-08       Impact factor: 4.460

8.  Novel indices for left-ventricular dyssynchrony characterization based on highly automated segmentation from real-time 3-d echocardiography.

Authors:  Honghai Zhang; Ademola K Abiose; Dipti Gupta; Dwayne N Campbell; James B Martins; Milan Sonka; Andreas Wahle
Journal:  Ultrasound Med Biol       Date:  2012-11-08       Impact factor: 2.998

9.  GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.

Authors:  Xinjian Chen; Jayaram K Udupa; Abass Alavi; Drew A Torigian
Journal:  Comput Vis Image Underst       Date:  2013-05       Impact factor: 3.876

10.  4-D cardiac MR image analysis: left and right ventricular morphology and function.

Authors:  Honghai Zhang; Andreas Wahle; Ryan K Johnson; Thomas D Scholz; Milan Sonka
Journal:  IEEE Trans Med Imaging       Date:  2009-08-25       Impact factor: 10.048

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