Detecting melanoma in dermoscopy images using scale adaptive local binary patterns

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Riaz, F (Autor) Outra; Hassan, A (Autor) Outra; Javed, MY (Autor) Outra; Coimbra, M (Autor) FCUP

Ata de Conferência Internacional

Título: 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Páginas: 6758-6761

36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Chicago, IL, AUG 26-30, 2014

Indexação

Scopus - 36 Citações

Outras Informações

ID Authenticus: P-00G-51K

DOI: 10.1109/embc.2014.6945179

Abstract (EN): Recent advances in the area of computer vision has led to the development of various assisted diagnostics systems for the detection of melanoma in the patients. Texture and color are considered as two fundamental visual characteristics which are vital for the detection of melanoma. This paper proposes the use of a combination of texture and color features for the classification of dermoscopy images. The texture features consist of a variation of local binary pattern (LBP) in which the strength of the LBPs is used to extract scale adaptive patterns at each pixel, followed by the construction of a histogram. For color feature extraction, we used standard HSV histograms. The extracted features are concatenated to form a feature vector for an image, followed by classification using support vector machines. Experiments show that the proposed feature set exhibits good classification performance comparing favorably to other state-of-the-art alternatives. © 2014 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica