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Decomposition of medical image based on grade multivariate methodology

Grzegorek M.

Biocybernetics and Biomedical Engineering

2007

Vol. 27, no. 4

29-48

The paper presents disjoint decomposition of the set of pixels of a NMR image and also of its fragment suggested as interesting by a medical consultant. Each pixel is described by the value of gray level gl, gradient module gm and items constructed on the basis of gm and gradient modules of adjacent pixels. The obtained dataset with rows corresponding to pixels and columns corresponding to variables is then processed by the algorithm called GCCA (Grade Correspondence Cluster Analysis). This rearranges the initial ordering of pixels (and also of variables) and then divides the set of rows into a chosen number of clusters. Pixels in each cluster are visualized as a separate subimage. The resulting decomposition (an ordered sequence of sub images) depends on the choice of a threshold parameter b which strongly influences the comparison of gm with gradient modules in pixel's neighborhood. It is shown how b should be selected to specify the edge of the lateral ventricle and to investigate homogeneity of gm's neighborhoods in the area indicated for the consultant.