A System for Computer Aided Detection of Diseases Patterns in High Resolution CT Images of the Lungs

  • Authors:

  • T. Zrimec;S. Busayarat

  • Affiliations:

  • University of New South Wales, Australia;University of New South Wales, Australia

  • Venue:
  • CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
  • Year:
  • 2007

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Abstract

Automatic detection of disease patterns in medical images can assist radiologists in image analysis. We present a system for detection of disease patterns demonstrated on HRCT images of the lung. Automated image analysis can be assisted by incorporating into a program information and knowledge that is available to radiologists. Anatomical features and landmarks are first extracted from the images. This information, together with the structure and regions of the lung, that are stored in a model of the lungs, is used in detecting disease patterns. Rules for recognizing different disease patterns are generated using machine learning. The system's performance is demonstrated on detecting two kinds of diseases patterns, one related to structural deformation of the bronchial tree and one showing fibrotic changes of the lung parenchyma. The results show that the system is able to recognize and indicate the existence, size and location of potential lung abnormalities.