Author, as appears in the article.: Mohamed Abdel-Nasser; Jaime Melendez; Antonio Moreno; Domenec Puig
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: ABDELNASSER MOHAMED MAHMOUD, MOHAMED; Jaime Melendez; MORENO RIBAS, ANTONIO; PUIG VALLS, DOMÈNEC SAVI
Keywords: Pixels mammography image processing
Abstract: 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.
Research group: ITAKA: Tecnologies Intel.ligents Avançades per a la Gestió del Coneixement Robòtica i Visió Intel.ligents
Thematic Areas: Computer engineering Ingeniería informática Enginyeria informàtica
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 1687-9384
Author identifier: 0000-0002-1074-2441; 0000-0003-1066-9536; 0000-0003-3945-2314; 0000-0002-0562-4205
Record's date: 2016-05-23
Journal volume: 2016
Papper version: info:eu-repo/semantics/publishedVersion
Article's DOI: 10.1155/2016/1370259
Entity: Universitat Rovira i Virgili
Journal publication year: 2016
First page: Article number 1370259
Publication Type: Article Artículo Article
Repositori URV