New auto-segment method of cerebral hemorrhage

Weijiang Wang*, Tingzhi Shen, Hua Dang

*Corresponding author for this work

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

Abstract

A novel method for Computerized tomography (CT) cerebral hemorrhage (CH) image automatic segmentation is presented in the paper, which uses expert system that models human knowledge about the CH automatic segmentation problem. The algorithm adopts a series of special steps and extracts some easy ignored CH features which can be found by statistic results of mass real CH images, such as region area, region CT number, region smoothness and some statistic CH region relationship. And a seven steps' extracting mechanism will ensure these CH features can be got correctly and efficiently. By using these CH features, a decision tree which models the human knowledge about the CH automatic segmentation problem has been built and it will ensure the rationality and accuracy of the algorithm. Finally some experiments has been taken to verify the correctness and reasonable of the automatic segmentation, and the good correct ratio and fast speed make it possible to be widely applied into practice.

Original languageEnglish
Title of host publicationMIPPR 2007
Subtitle of host publicationMedical Imaging, Parallel Processing of Images, and Optimization Techniques
DOIs
Publication statusPublished - 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

  • Cerebral hemorrhage segment
  • Decision tree
  • Feature extraction

Fingerprint

Dive into the research topics of 'New auto-segment method of cerebral hemorrhage'. Together they form a unique fingerprint.

Cite this