Segmentation of medical images using geo-theoretic distance matrix in fuzzy clustering

Authors:
Tuan D. Pham;Uwe Eisenblätter;Jonathan Golledge;Bernhard T. Baune;Klaus Berger
Affiliations:
ADFA, School of Information Technology and Electrical Engineering, University of New South Wales, Canberra, ACT, Australia;ADFA, School of Information Technology and Electrical Engineering, University of New South Wales, Canberra, ACT, Australia;Vascular Biology Unit, School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia;Department of Psychiatry and Psychiatric Neuroscience, School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia;Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
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Abstract

Investigation on novel methods for extracting objects of interest in medical images has been an important and challenging area of research in image analysis. In particular, medical images are highly spatially correlated and subject to fuzzy distribution of pixels, we present in this paper a new algorithm for medical image segmentation with special reference to abdominal aortic aneurysm and degraded human brain imaging. Development of the new algorithm is based on the implementation of the theoretic distance matrix with spatial semivariances.