A Method for Identification and Visualization of Histological Image Structures Relevant to the Cancer Patient Conditions
A method is suggested for identification and visualization of histology image structures relevant to the key characteristics of the state of cancer patients. The method is based on a multi-step procedure which includes calculating image descriptors, extracting their principal components, correlating them to known object properties and mapping disclosed regularities all the way back up to the corresponding image structures they found to be linked with. Image descriptors employed are extended 4D color co-occurrence matrices counting the occurrence of all possible pixel triplets located at the vertices of equilateral triangles of different size. The method is demonstrated on a sample of 952 histology images taken from 68 women with clinically confirmed diagnosis of ovarian cancer. As a result, a number of associations between the patients’ conditions and morphological image structures were found including both easily explainable and the ones whose biological substrate remains obscured.
KeywordsOvarian Cancer Ovarian Cancer Patient Image Structure Image Mining Histological Image
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