Automatic nipple detection in breast thermograms. (1st December 2016)

• Record Type: Journal Article
• Authors: Abdel-Nasser, Mohamed; Saleh, Adel; Moreno, Antonio; Puig, Domenec
• Is Part Of: Expert systems with applications. Volume 64(2016)
• Page Start: 365
• Page End: 374
• Publication Date: 2016-12-01
• Subjects: Breast cancer -- Infrared -- Thermograms -- Nipple detection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.08.026
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose an automatic and accurate method to detect nipples in thermograms. The proposed method determines the nipples using a novel selection algorithm. We achieve accurate nipple detection results in real-time. Abstract: Breast cancer is one of the most dangerous diseases for women. Detecting breast cancer in its early stage may lead to a reduction in mortality. Although the study of mammographies is the most common 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 proposed. Breast cancer CAD systems consist of many steps, such as segmentation of the region of interest, feature extraction, classification and nipple detection. Indeed, the nipple is an important anatomical landmark in thermograms. The location of the nipple is invaluable in the analysis of medical images because it can be used in several applications, such as image registration and modality fusion. This paper proposes an unsupervised, automatic, accurate, simple and fast method 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 dataset 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 detection 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 application, achieving good results.