Articles producció científica> Enginyeria Informàtica i Matemàtiques

The impact of pixel resolution, integration scale, preprocessing, and feature normalization on texture analysis for mass classification in mammograms

  • Identification data

    Identifier: imarina:9282609
  • Authors:

    Abdel-Nasser M
    Melendez J
    Moreno A
    Puig D
    International Journal Of Optics
    10.1155/2016/1370259
    International Journal Of Optics. 2016
  • Others:

    Author, as appears in the article.: Abdel-Nasser M; Melendez J; Moreno A; Puig D
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Moreno Ribas, Antonio / Puig Valls, Domènec Savi
    Keywords: X ray screens Texture analysis method Support vector machines Spatial arrangements Sequential forward selection Pixels Nonlinear support vector machines Mass classifications Mammography Image segmentation Image processing Feature normalization Classification performance Classification (of information) Breast density estimation
    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. © 2016 Mohamed Abdel-Nasser et al.
    Thematic Areas: Optics Electronic, optical and magnetic materials Atomic and molecular physics, and optics
    Author's mail: mohamed.abdelnasser@urv.cat antonio.moreno@urv.cat domenec.puig@urv.cat
    Author identifier: 0000-0002-1074-2441 0000-0003-3945-2314 0000-0002-0562-4205
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.hindawi.com/journals/ijo/2016/1370259/
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Optics. 2016
    APA: Abdel-Nasser M; Melendez J; Moreno A; Puig D (2016). The impact of pixel resolution, integration scale, preprocessing, and feature normalization on texture analysis for mass classification in mammograms. International Journal Of Optics, 2016(), -. DOI: 10.1155/2016/1370259
    Article's DOI: 10.1155/2016/1370259
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2016
    Publication Type: Journal Publications
  • Keywords:

    Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Optics
    X ray screens
    Texture analysis method
    Support vector machines
    Spatial arrangements
    Sequential forward selection
    Pixels
    Nonlinear support vector machines
    Mass classifications
    Mammography
    Image segmentation
    Image processing
    Feature normalization
    Classification performance
    Classification (of information)
    Breast density estimation
    Optics
    Electronic, optical and magnetic materials
    Atomic and molecular physics, and optics
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