Literature DB >> 18003192

Joint segmentation-registration of organs using geometric models.

Alper Ayvaci1, Daniel Freedman.   

Abstract

In this paper, we present a novel method for the segmentation of the organs found in CT and MR images. The proposed algorithm utilizes the shape model of the target organ to gain robustness in the case where the objective organ is surrounded by other organs or tissue with the similar intensity profile. The algorithm labels the image based on the graph-cuts technique and incorporates the shape prior using a technique based on level-sets. The method requires proper registration of the shape template for an accurate segmentation, and we propose a unified registration-segmentation framework to solve this problem. Furthermore, to reduce the computational cost, the algorithm is designed to run on watershed regions instead of voxels. The accuracy of the algorithm is shown on the medical examples.

Mesh:

Year:  2007        PMID: 18003192     DOI: 10.1109/IEMBS.2007.4353526

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  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

2.  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

3.  Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy.

Authors:  Zhong Xue; Kelvin Wong; Stephen T C Wong
Journal:  Comput Med Imaging Graph       Date:  2009-08-25       Impact factor: 4.790

  3 in total

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