Skip to main navigation Skip to search Skip to main content

Segmentation of medical ultrasound images: Novel level set approach

  • Zhuhuang Zhou
  • , Tianfu Wang*
  • , Jiangli Lin
  • , Deyu Li
  • , Changqiong Zheng
  • *Corresponding author for this work
  • Sichuan University
  • Shenzhen University

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

Abstract

In this paper, a novel level set approach is proposed for segmentation of medical ultrasound images. Considering the speckle noise and low contrast of medical ultrasound images, we add an extra stopping term into the formulation of level set evolution without re-initialization (LSEWR). Compared with traditional active contour models, the proposed level set approach has more flexible initialization and larger capture range. It is insensitive to the initial contour and larger time step can be used. The initial contour can be easily initialized as a circle or rectangular, thus achieving semi-automatic segmentation of ultrasound medical images. The experimental results show that the proposed method can be used for semi-automatic and high-quality segmentation of medical ultrasound images.

Original languageEnglish
Title of host publicationMIPPR 2007
Subtitle of host publicationMedical Imaging, Parallel Processing of Images, and Optimization Techniques
DOIs
StatePublished - 2007
EventMIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques - Wuhan, China
Duration: 15 Nov 200717 Nov 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6789
ISSN (Print)0277-786X

Conference

ConferenceMIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Country/TerritoryChina
CityWuhan
Period15/11/0717/11/07

Keywords

  • Active contour model
  • Level set
  • Medical ultrasound image
  • Segmentation

Fingerprint

Dive into the research topics of 'Segmentation of medical ultrasound images: Novel level set approach'. Together they form a unique fingerprint.

Cite this