PHOC Descriptor Applied for Mammography Classification

Abstract

This paper describes experiments with PHOC (Pyramid Histogram of Color) features descriptor in terms of capacity for representing features presented in breast radiograph (also known as mammography). Patches were taken from regions in digital mammographies, representing benign, cancerous, normal tissues and image’s background. The motivation is to evaluate the proposal in perspective of using it for execution in an inexpensive ordinary desktop computer in places located far from medical experts. The images were obtained from DDSM database and processed producing the feature-dataset used for training an Artificial Neural Network, the results were evaluated by analysis of the learning rate curve and ROC curves, besides these graphical analytical tools the confusion matrix and other quantitative metrics (TPR, FPR and Accuracy) were also extracted and analyzed. The average accuracy ≈ 0.8 and the other metrics extracted from results demonstrate that the proposal presents potential for further developments. At the best effort, PHOC was not found in literature for applications in mammographies such as it is proposed here.