Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

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Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

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  • DOI:  10.1049/cp:20060551
  • ISBN: 0 86341 671 3
  • Location: Bangalore, India
  • Conference date: 26-28 Sept. 2006
  • Format: PDF
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. A robust segmentation technique based on fuzzy set theory for brain MR images is proposed in this paper. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through second order fuzzy correlation. To calculate the second order fuzzy correlation, a modified co-occurrence matrix is used to extract the local information more accurately. Two parameters - ambiguity and the strength of ambiguity, are introduced to determine the thresholds of the given histogram. The effectiveness of the proposed algorithm, along with a comparison with other methods, has been demonstrated on a set of brain MR images.

Inspec keywords: correlation methods; matrix algebra; fuzzy set theory; image segmentation; biomedical MRI; medical image processing

Subjects: Combinatorial mathematics; Biomedical magnetic resonance imaging and spectroscopy; Combinatorial mathematics; Patient diagnostic methods and instrumentation; Medical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Biology and medical computing; Optical, image and video signal processing; Algebra, set theory, and graph theory

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