Hierarchical segmentation of CT head images

Sven Loncaric, Dubravko Cosic, Atam P. Dhawan

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations


Quantitative analysis of head images obtained by computed tomography (CT) requires accurate segmentation. A new method for automatic segmentation of human spontaneous intracerebral brain hemorrhage (ICH) from digitized CT films is presented in the paper. The proposed segmentation method has a two-level hierarchical structure. The segmentation at both levels is based on the unsupervised fuzzy C-means (UFCM) clustering algorithm but using different feature vectors. At the higher hierarchical level, clusters obtained by UFCM algorithm consist of several disconnected image regions. An image labeling algorithm labels each image region with one of the following labels: background, skull, brain, and ICH to obtain the global image segmentation. At the lower hierarchical level the brain region is further segmented using UFCM clustering to obtain the edema region and the ventricle region. The advantage of the method is that it is not sensitive to variability in image brightness. The method has been tested on real CT images and has performed correct segmentation.

Original languageEnglish (US)
Pages (from-to)736-737
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 1996
Externally publishedYes
EventProceedings of the 1996 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 2 (of 5) - Amsterdam, Neth
Duration: Oct 31 1996Nov 3 1996

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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