Tissue type detection by block processing

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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.

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