Tissue type detection by block processing

Tianhu Lei; Zuo Zhao; Wilfred Sewchand

doi:10.1117/12.175081

11 May 1994 Tissue type detection by block processing

Tianhu Lei, Zuo Zhao, Wilfred Sewchand

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175081
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

A new region detection and segmentation method is presented for performing tissue type classification and quantification. The original image data are transformed to the samples of a sample vector. The covariance matrix of this sample vector and its eigenvalues are computed. These eigenvalues are inputed into the information criterion of minimum description length to determine the region numbers. Then a modified K-mean algorithm and Bayesian classifier are utilized to segment image into the regions. This method does not need image model, considers the spatial correlations among the pixels, and is much faster than the model- based approaches.

Citation Download Citation

Tianhu Lei, Zuo Zhao, and Wilfred Sewchand "Tissue type detection by block processing", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175081

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