A method for segmentation of CT head images

  • Sven Lončarić
  • Domagoj Kovačević
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


A novel method for automatic segmentation of computed tomography (CT) head images of patients having spontaneous intracerebral brain hemorrhage has been presented in this work. The method consists of four major phases. The first phase performs a brightness normalization of a CT image by applying a K-means clustering algorithm to the pixel brightness values. A feature extraction based on a special receptive field is done in the second phase. The third phase performs a pixel classification by means of a feed-forward error-back propagation neural network. In the last phase, a rule-based expert system is used to perform image labeling. The proposed method has been applied to real patient CT images and has shown encouraging results.


Compute Tomography Image Receptive Field Cluster Center Automatic Segmentation Brightness Normalization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Sven Lončarić
    • 1
  • Domagoj Kovačević
    • 1
  1. 1.Department of Electronic Systems and Information ProcessingFaculty of Electrical Engineering and Computing University of ZagrebZagrebCroatia

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