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Proceedings Paper

Segmentation of magnetic resonance image using fractal dimension
Author(s): Joseph K. K. Yau; Sau-hoi Wong; Kwok-Leung Chan
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Paper Abstract

In recent years, much research has been conducted in the three-dimensional visualization of medical image. This requires a good segmentation technique. Many early works use first-order and second-order statistics. First-order statistical parameters can be calculated quickly but their effectiveness is influenced by many factors such as illumination, contrast and random noise of the image. Second-order statistical parameters, such as spatial gray level co-occurrence matrices statistics, take longer time to compute but can extract the textural information. In this investigating, two different parameters, namely the entropy and the fractal dimension, are employed to perform segmentation of the magnetic resonance images of the head of a male cadaver. The entropy is calculated from the spatial gray level co-occurrence matrices. The fractal dimension is calculated by the reticular cell counting method. Several regions of the human head are chosen for analysis. They are the bone, gyrus and lobe. Results show that the parameters are able to segment different types of tissue. The entropy gives very good result but it requires very long computation time and large amount of memory. The performance of the fractal dimension is comparable with the entropy. It is simple to estimate and demands lesser memory space.

Paper Details

Date Published: 25 April 1997
PDF: 11 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274101
Show Author Affiliations
Joseph K. K. Yau, City Univ. of Hong Kong (Hong Kong)
Sau-hoi Wong, City Univ. of Hong Kong (Hong Kong)
Kwok-Leung Chan, City Univ. of Hong Kong (Hong Kong)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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