Wavelet-based Texture Analysis in Multiple Sclerosis using Quantitative MRI

D.-L. Feis, H. Neeb, and I. Weinreich (Germany)


Wavelets, Image Processing, Medicine, Segmentation and Representation, Medical Image Analysis


This paper deals with an application of the two dimensional wavelet transform to MRI data. The main goal is to employ the DWT for finding peaks and irregular features in the white matter of brain scans of multiple sclerosis patients. After masking the region of interest a one level wavelet transform already yields some interesting hints for the distinction between healthy and diseased probands. The comparison of histograms of wavelet coefficients between both groups obviously shows two different distributions. A hypothesis about the different shapes of these distributions might give interesting additional information with respect to classifying the MRI data

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