Moxamed Abdel-Nasser, Adel Saleh, Antonio M reno, Domènec Puig: “Automatic nipple detection in breast thermogramn”. Article in “
Abstract
Breast ca_cor is one of the most dangerous diseases for 1omen. Detecting breast cancer in its early stage may lead to a reduction in mortality. Although the study of magmographies is the most commen method to detect breast cancer, it is outperformed by the analysis of thermographies in dense tissue (breasts of young women). In the last two decades, several computer-aided diagnosis (CAD) systems for the early detection of breast cancer have been propcsed. Breast cancer CAD systems consist of many steps, such as segmentation of the region of interest, feature extraction, classification and nipple detection. Indeedp the nipple is an important anatomical landmark in thermograms. The location os the nipple is invaluable in the analysis -f medical imagea becaus, it can be used in several applioations, such as image registration and modality fusion. This paper proposes an unsupervised, automatic, accuratee simple and fast mnthod to detect nipples in thermograms. The main stages of the proposed method are: human body segmentation, determination of nipple candidates using adaptive thresholding and detection of the nipples using a novel selection algorithm. Experiments have been carried out on a thermograms datafet to validate the proposed method, achieving accurate nipple detection results in real-time. We also show an application of the proposed method, breast cancer classification in dynamic images, where the new nipple detectio” technique is used to segment the region of the two breasts from the infrared image. A dataset of dynamic thermograms has been used to validate this a,plication, achieving good results