Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

• Type: Article in International Scientific Journal
• Date: 2009

Authors

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

Journal

Title: IEEE Journal of Selected Topics in Signal Processing

Vol. 3

Pages: 35-45

ISSN: 1932-4553

Indexing

ISI Web of Knowledge - 249 Citations

Scopus - 369 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11