Segmentation of gallbladder from CT images for a surgical training system

Jiayin Zhou, Weimin Huang, Jing Zhang, Tao Yang, Jiang Liu, Chee Kong Chui, Stephen Chang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations, was projected to the neighboring slices for automated re-sampling, learning and further voxel classification in these slices. This propagation procedure continued till all GB-containing slices were processed. The method was tested using 18 CT data sets and a set of quantitative measures were computed. The averaged volume overlap error of 15.56% and surface distance of 0.64 mm suggested that the method is efficient and promising.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Pages536-540
Number of pages5
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010 - Yantai, China
Duration: 16 Oct 201018 Oct 2010

Publication series

NameProceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Volume2

Conference

Conference3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010
Country/TerritoryChina
CityYantai
Period16/10/1018/10/10

Keywords

  • Gallbladder
  • Image segmentation
  • Image-guided surgical training
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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