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Proceedings Paper

Tissue classification by wavelet modified generic Fourier descriptor and their recognition using hybrid correlator
Author(s): Raj Bahadur Yadav; Arun K. Gupta
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Paper Abstract

Segmentation in Magnetic resonance imaging (MRI) images is a widely studied problem, and techniques (supervised and unsupervised) are discussed in the literature. The basic approaches to image segmentation are based upon: (a) boundary representation, (b) regional characteristics and (c) a combination of boundary and region-based features. In this paper, we report classification of brain tissue based objects employing one of combination of boundary and region-based features as wavelet modified generic Fourier descriptor (WGFD) technique. This technique have been applied to a database consisting of 3 different class's tissues, each class consist of 50 shapes. The Euclidean distance has been calculated as a similarity measure parameter for tissue shape classification. The classification results have been carried out and it is inferred that WGFD performs for brain tissue classification. For brain tissue recognition, a simulation experiment employing hybrid correlator architecture has been carried out. We have used Wavelet modified maximum average correlation hight (MACH) filter for hybrid correlator. Mexican-hat wavelet has used to synthesize the wavelet MACH filter for simulation experiment.

Paper Details

Date Published: 23 February 2010
PDF: 7 pages
Proc. SPIE 7564, Photons Plus Ultrasound: Imaging and Sensing 2010, 75642R (23 February 2010); doi: 10.1117/12.841570
Show Author Affiliations
Raj Bahadur Yadav, The Univ. of Electro-Communications (Japan)
Arun K. Gupta, Instruments Research and Development Establishment (India)


Published in SPIE Proceedings Vol. 7564:
Photons Plus Ultrasound: Imaging and Sensing 2010
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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