Histopathological image classification with the bag of words model

Colon cancer, which is one of the most common cancer type, could be cured with its early diagnosis. In the current practice of medicine, there are many screening techniques such as colonoscopy, sigmoidoscopy, and stool test, however the most effective and most widely used method for cancer diagnosis is to take tissue sections with biopsy and examine them under a microscope. As this examination is based on visual interpretation, it may lead to subjective decisions and diagnostic differences among pathologists. The need of reducing inter-variability in cancer diagnosis has led to studies for extraction of features from biopsy images and development of algorithms that give objective results. In this paper, we propose a method for the automated classification of a colon tissue image with the features extracted from a histogram that models the existence of image regions determined in an unsupervised way. Experiments on colon tissue images show that the proposed method leads to more successful results compared to its counterparts. Moreover, the proposed method, which uses color intensities for feature extraction, has the potential of giving better results with the use of additional features.