Automatic chronic degenerative diseases identification using enteric nervous system images

Cancer Identification in Walker 256 Tumor Model Exploring Texture Properties Taken from Microphotograph of Rats Liver

Recent studies have been evaluating the presence of patterns associated with the occurrence of cancer in different types of tissue present in the individual affected by the disease. In this article, we describe preliminary results for the automatic detection of cancer (Walker 256 tumor) in laboratory animals using preclinical microphotograph images of the subject’s liver tissue. In the proposed approach, two different types of descriptors were explored to capture texture properties from the images, and we also evaluated the complementarity between them. The first texture descriptor experimented is the widely known Local Phase Quantization (LPQ), which is a descriptor based on spectral information. The second one is built by the application of a granulometry given by a family of morphological filters. For classification, we have evaluated the algorithms Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Logistic Regression. Experiments carried out on a carefully curated dataset developed by the Enteric Neural Plasticity Laboratory of the State University of Maringá showed that both texture descriptors provide good results in this scenario. The accuracy rates obtained using the SVM classifier were 96.67% for the texture operator based on granulometry and 91.16% for the LPQ operator. The dataset was made available also as a contribution of this work. In addition, it is important to remark that the best overall result was obtained by combining classifiers created using both descriptors in a late fusion strategy, achieving an accuracy of 99.16%. The results obtained show that it is possible to automatically perform the identification of cancer in laboratory animals by exploring texture properties found on the tissue taken from the liver. Moreover, we observed a high level of complementarity between the classifiers created using LPQ and granulometry properties in the application addressed here.