Evaluation of active contour on medical inhomogeneous image segmentation

Yun Jen Chiu, Van Truong Pham, Thi Thao Tran, Kuo Kai Shyu

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

3 Scopus citations

Abstract

Segmentation is an important step in medical image analysis. This process is crucial but challenging due to inhomogeneneity in intensity of images. In addition, the images are often corrupted by noise and with contrast edges. There are some approaches aiming to cope with this kind of images such as: region growing, region competition, watershed segmentation, global thresholding, and active contour methods. Among them, active contour methods, especially level set-based active contour is widely used for image segmentation by their advantageous properties such as topology adaptability, and robustness to initialization. In this paper, we present and demonstrate the effectiveness of some recently active contour models for segmenting medical images with inhomogeneity in intensity. Among these techniques, the local binary fitting based model is validated as a promising method for medical image segmentation.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Pages311-314
Number of pages4
DOIs
StatePublished - 2010
Event2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010 - Chengdu, China
Duration: 9 Jul 201011 Jul 2010

Publication series

NameProceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Volume1

Conference

Conference2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Country/TerritoryChina
CityChengdu
Period9/07/1011/07/10

Keywords

  • Active contour
  • Intensity inhomogeneity image segmentation
  • Level set method
  • Medical image segmentation

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