Literature DB >> 22947429

An improved method of automatic colon segmentation for virtual colon unfolding.

Lin Lu1, Jun Zhao.   

Abstract

The technique of virtual colon unfolding (VU) is a non-invasive procedure to detect polyps on the colon inner wall. Compared with conventional virtual colonoscopy, VU is faster and results in fewer uninspected regions. However, the performance of VU is more vulnerable to the quality of colon segmentation. In this paper, an improved colon segmentation method is proposed to enhance the performance of VU. The improved method is with the use of a novel post-processing scheme, which is composed of two parts: attain more accurate centerlines with the help of scalar complementary geodesic distance field and compensate gap-like artifacts based on local morphological information. We validated the improved method on twenty colon cases via two widely used VU techniques, the ray-casting technique and the conformal-mapping technique. Experimental results indicated that with the use of the improved method, the rates of correct response via ray-casting and conformal-mapping techniques were respectively elevated by 14.9% and 13.1%, while the rates of false response were respectively reduced by 8.4% and 10.8%.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22947429     DOI: 10.1016/j.cmpb.2012.08.012

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  4 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

2.  Automatic segmentation of colon in 3D CT images and removal of opacified fluid using cascade feed forward neural network.

Authors:  K Gayathri Devi; R Radhakrishnan
Journal:  Comput Math Methods Med       Date:  2015-03-09       Impact factor: 2.238

3.  An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography.

Authors:  Weiguo Cao; Marc J Pomeroy; Shu Zhang; Jiaxing Tan; Zhengrong Liang; Yongfeng Gao; Almas F Abbasi; Perry J Pickhardt
Journal:  Sensors (Basel)       Date:  2022-01-25       Impact factor: 3.576

4.  Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Authors:  Yesenia Gonzalez; Chenyang Shen; Hyunuk Jung; Dan Nguyen; Steve B Jiang; Kevin Albuquerque; Xun Jia
Journal:  Med Image Anal       Date:  2020-12-16       Impact factor: 8.545

  4 in total

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