Evaluation of textural feature extraction schemes for neural network-based interpretation of regions in medical images

A few approaches have been presented in the literature towards the discrimination of texture in medical images. Recently, medical experts proposed that the more valuable information for discriminating among normal and suspicious for cancer regions in endoscopic images is the texture of the examined tissue. Texture can be encoded by a number of mathematical descriptors. Three well-known textural descriptors, as well as a new wavelet-based one are used in this paper for an accurate study and evaluation of the methodologies encountered. Experiments conducted include tests with various images from the Brodatz album, as well as interpretation of tissue regions in endoscopic image. In all cases the recognition task is supported by multilayer perceptron type neural network architectures.