Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

Margarida Silveira (Autor) Outra; Jacinto C Nascimento (Autor) Outra; Jorge S Marques (Autor) Outra; Andre R S Marcal (Autor) FCUP; Teresa Mendonca (Autor) FCUP; Syogo Yamauchi (Autor) Outra; Junji Maeda (Autor) Outra; Jorge Rozeira (Autor) Outra

Revista

Título: IEEE Journal of Selected Topics in Signal Processing

Vol. 3

Páginas: 35-45

ISSN: 1932-4553

Indexação

ISI Web of Knowledge - 249 Citações

Scopus - 369 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-003-NF1

DOI: 10.1109/jstsp.2008.2011119

Abstract (EN): In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al. [(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set [(EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11