Articles producció científica> Enginyeria Informàtica i Matemàtiques

A probabilistic approach for breast boundary extraction in mammograms

  • Dades identificatives

    Identificador: PC:540
  • Autors:

    Aghdam, H.H.
    Puig, D.
    Solanas, A.
  • Altres:

    Autor segons l'article: Aghdam, H.H. Puig, D. Solanas, A.
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: http://www.hindawi.com/journals/cmmm/2013/408595/
    Departament: Enginyeria Informàtica i Matemàtiques
    DOI de l'article: 10.1155/2013/408595
    Resum: The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.
    Entitat: Universitat Rovira i Virgili.
    Àrees temàtiques: Mammography
    Any de publicació de la revista: 2013
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1748-6718
    Volum de revista: 2013