Autor según el artículo: Mohamed Abdel-Nasser; Jaime Melendez; Antonio Moreno; Domenec Puig

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: ABDELNASSER MOHAMED MAHMOUD, MOHAMED; Jaime Melendez; MORENO RIBAS, ANTONIO; PUIG VALLS, DOMÈNEC SAVI

Palabras clave: Pixels mammography image processing

Resumen: Texture analysis methods are widely used to characterize breast masses in mammograms. Texture gives information about the spatial arrangement of the intensities in the region of interest. This information has been used in mammogram analysis applications such as mass detection, mass classification, and breast density estimation. In this paper, we study the effect of factors such as pixel resolution, integration scale, preprocessing, and feature normalization on the performance of those texture methods for mass classification. The classification performance was assessed considering linear and nonlinear support vector machine classifiers. To find the best combination among the studied factors, we used three approaches: greedy, sequential forward selection (SFS), and exhaustive search. On the basis of our study, we conclude that the factors studied affect the performance of texture methods, so the best combination of these factors should be determined to achieve the best performance with each texture method. SFS can be an appropriate way to approach the factor combination problem because it is less computationally intensive than the other methods.

Grupo de investigación: ITAKA: Tecnologies Intel.ligents Avançades per a la Gestió del Coneixement Robòtica i Visió Intel.ligents

Áreas temáticas: Computer engineering Ingeniería informática Enginyeria informàtica

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 1687-9384

Identificador del autor: 0000-0002-1074-2441; 0000-0003-1066-9536; 0000-0003-3945-2314; 0000-0002-0562-4205

Fecha de alta del registro: 2016-05-23

Volumen de revista: 2016

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

DOI del artículo: 10.1155/2016/1370259

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2016

Página inicial: Article number 1370259

Tipo de publicación: Article Artículo Article