Close
Metadata

@InProceedings{ZampirolliStraLorePaul:2010:ApGrAn,
               author = "Zampirolli, Francisco de Assis and Stransky, Beatriz and Lorena, 
                         Ana Carolina and Paulon, F{\'a}bio Luis de Melo",
          affiliation = "{Universidade Federal do ABC} and {Universidade Federal do ABC} 
                         and {Universidade Federal do ABC} and {Universidade Federal do 
                         ABC}",
                title = "Segmentation and classification of histological images - 
                         application of graph analysis and machine learning methods",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "image analysis, mathematical morphology, graph analysis, machine 
                         learning, tissue.",
             abstract = "The characterization and quantitative description of histological 
                         images is not a simple problem. To reach a final diagnosis, 
                         usually the specialist relies on the analysis of characteristics 
                         easily observed, such as cells size, shape, staining and texture, 
                         but also depends on the hidden information of tissue localization, 
                         physiological and pathological mechanisms, clinical aspects, or 
                         other etiological agents. In this paper, Mathematical Morphology 
                         (MM) and Machine Learning (ML) methods were applied to 
                         characterize and classify histological images. MM techniques were 
                         employed for image analysis. The measurements obtained from image 
                         and graph analysis were fed into Machine Learning algorithms, 
                         which were designed and developed to automatically learn to 
                         recognize complex patterns and make intelligent decisions based on 
                         data. Specifically, a linear Support Vector Machine (SVM) was used 
                         to evaluate the discriminatory power of the used measures. The 
                         results show that the methodology was successful in characterizing 
                         and classifying the differences between the architectural 
                         organization of epithelial and adipose tissues. We believe that 
                         this approach can be also applied to classify and help.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "article_sibgrapi_v8.pdf",
        urlaccessdate = "2020, Nov. 25"
}


Close