Nuclei Graph Local Features For Basal Cell Carcinoma Classification In Whole Slide Images

Evidence based medicine aims to provide a quantifiable framework to support cancer optimal treatment selection. Pathological examination is the main evidence used in medical management, yet the level of quantification is low and highly dependent on the examiner's expertise. This paper presents and evaluates a method to extract graph based topological features from skin tissue images to identify cancerous regions associated to basal cell carcinoma. These graph features constitute a quantitative measure of the architectural tissue organization. Results show that graph topological features extracted from a nuclei based distance graph, particularly those related to local density, have a high predictive value in the automated detection of basal cell carcinoma. The method was evaluated using a leave-one-out validation scheme in a set of 9 skin Whole Slide Images obtaining an average F-score of 0.72 in distinguishing basal cell carcinoma regions in skin tissue whole slide images.