Autor segons l'article: Abdel-Nasser M; Saleh A; Moreno A; Puig D
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Moreno Ribas, Antonio / Puig Valls, Domènec Savi
Paraules clau: Adaptive thresholding Breast cancer Breast cancer cad systems Breast cancer classifications Computer aided diagnosis Computer aided diagnosis(cad) Diagnosis Diseases Early detection of breast cancer Feature extraction Image segmentation Infrared Infrared imaging Infrared radiation Medical imaging Nipple detection Temperature measuring instruments Thermograms Thermography (temperature measurement)
Resum: 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. © 2016 Elsevier Ltd
Àrees temàtiques: Administração pública e de empresas, ciências contábeis e turismo Administração, ciências contábeis e turismo Arquitetura, urbanismo e design Artificial intelligence Astronomia / física Biodiversidade Biotecnología Ciência da computação Ciências agrárias i Ciências ambientais Ciências biológicas i Ciências biológicas ii Ciências biológicas iii Ciências sociais aplicadas i Computer science applications Computer science, artificial intelligence Direito Economia Educação Enfermagem Engenharias i Engenharias ii Engenharias iii Engenharias iv Engineering (all) Engineering (miscellaneous) Engineering, electrical & electronic Farmacia General engineering Geociências Interdisciplinar Matemática / probabilidade e estatística Materiais Medicina i Medicina ii Medicina iii Operations research & management science Química
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: antonio.moreno@urv.cat domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
Identificador de l'autor: 0000-0003-3945-2314 0000-0002-0562-4205 0000-0002-1074-2441
Data d'alta del registre: 2023-05-14
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S095741741630416X?via%3Dihub
Referència a l'article segons font original: Expert Systems With Applications. 64 365-374
Referència de l'ítem segons les normes APA: Abdel-Nasser M; Saleh A; Moreno A; Puig D (2016). Automatic nipple detection in breast thermograms. Expert Systems With Applications, 64(), 365-374. DOI: 10.1016/j.eswa.2016.08.026
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.1016/j.eswa.2016.08.026
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2016
Tipus de publicació: Journal Publications
Repositori URV