Classification of colorectal cancer based on the association of multidimensional and multiresolution features

Abstract

Colorectal cancer is one of the most common types of cancer according to worldwide incidences statistics. The correct diagnosis of this lesion leads to the indication of the most adequate treatments for cancer-affected patients. The diagnosis is made through the visual analysis of tissue samples by pathologists. However, this analysis is susceptible to intra- and inter-pathologists variability in addition to being a complex and time-consuming task. To deal with these challenges, image processing methods are developed for application on histological images obtained through the digitization of the tissue samples. To do so, feature extraction and classification techniques are investigated to aid pathologists and make it possible a faster and more objective diagnosis definition. Therefore, in this work, we propose a method that associates multidimensional fractal techniques, curvelet transforms and Haralick descriptors for the study and pattern recognition of colorectal cancer, which not yet explored in the Literature. The proposed method considered a feature selection approach and different classification techniques for evaluating associations, such as decision tree, random forest, support vector machine, naive Bayes, k* and a polynomial method. This strategy allowed for more precise interpretations regarding the best associations for the separation of groups concerning histological images of colorectal cancer. The proposal was tested on colorectal images from two distinct datasets commonly investigated in the Literature. The best result was reached with features based mainly on lacunarity and percolation obtained from curvelet sub-images, using a polynomial classifier. The tests were evaluated by applying the 10-fold cross-validation method and the result was 0.994 of AUC, which is a relevant contribution to the Literature of pattern recognition of colorectal cancer. The obtained performance with a detailed analysis involving different types of features and classifiers are important contributions for pathologists, specialists interested in the study of this cancer and histological image processing researchers, which aim to develop the clinically applicable computational techniques.