Mathematical analysis of colon glands for cancer diagnosis

Neoplastic diseases including cancer cause organizational changes in tissues. Histopathological examination, which is routinely used for the diagnosis and grading of these diseases, relies on pathologists to identify such tissue changes under a microscope. However, as this examination mainly relies on the visual interpretation of pathologists, it may lead to a considerable amount of subjectivity. To reduce the subjectivity level, it is proposed to use computational methods that provide objective measures. These methods quantify the tissue changes associated with disease by defining features on tissue images. In this paper, colon glands are mathematically analyzed making use of different feature extraction approaches. In this analysis, morphological, intensity-based, and textural features are investigated and glands are classified using these features. Working on the images of 108 colon tissues of 36 patients, our experiments demonstrate that this classification leads to promising results for differentiating normal glands from the cancerous ones.