Comparative analysis of codeword representation by clustering methods for the classification of histological tissue types

Abstract

In this study, the classification of several histological tissue types, i.e., muscles, nerves, connective and epithelial tissue cells, is studied in high resolutional histological images. In the feature extraction step, bag of features method is utilized to reveal distinguishing features of each tissue cell types. Local small blocks of sub-images/patches are extracted to find discriminative patterns for followed strategy. For detecting points of interest in local patches, Harris corner detection method is applied. Afterwards, discriminative features are extracted using the scale invariant feature transform method using these points of interests. Several code word representations are obtained by clustering approach (using k-means fuzzy c-means, expectation maximization method, Gaussian mixture models) and evaluated in comparative manner. In the last step, the classification of the tissue cells data are performed using k-nearest neighbor and support vector machines methods.