@InProceedings{Santos:2021:SeSeSk,
               author = "Santos, Elineide Silva dos",
          affiliation = "{Federal University of Piau{\'{\i}}}",
                title = "Semi-automatic Segmentation of Skin Lesions based on Superpixels 
                         and Hybrid Texture Information",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Dermatoscopic image segmentation. Computer-aided diagnosis. Skin 
                         lesion. Texture information.",
             abstract = "This article exposes a semi-automatic method with the potential to 
                         aid the doctor while supervising the progression of skin lesions. 
                         The proposed methodology pre-segments skin lesions using the SLIC0 
                         algorithm for the generation of superpixels. Following this, each 
                         superpixel is represented using a descriptor constructed of a mix 
                         from GLCM and Tamura texture features. The feature's gain ratios 
                         were utilized to choose the data applied in the semi-supervised 
                         clustering algorithm Seeded Fuzzy C-means. This algorithm uses 
                         certain specialist-marked regions to group the superpixels into 
                         lesion or background regions. Finally, the segmented image 
                         undergoes a post-processing step to eliminate sharp edges. The 
                         experiments were performed on a total of 3974 images. We used the 
                         2995 images from PH2, DermIS and ISIC 2018 datasets to establish 
                         our method's specifications and the 979 images from ISIC 2016 and 
                         ISIC 2017 datasets for performance analysis. Our experiments 
                         demonstrate that by manually identifying a few percentages of the 
                         generated superpixels, the proposed approach reaches an average 
                         accuracy of 95.97%, thus giving a superior performance to the 
                         techniques presented in the literature. Even though the proposed 
                         method requires physicians' intervention, they can obtain 
                         segmentation results similar to manual segmentation from a 
                         significantly less time-consuming task.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45E4SE5",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E4SE5",
           targetfile = "WTD___SIBGRAPI_2021___Elineide.pdf",
        urlaccessdate = "2024, May 06"
}