Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/JSTSP.2008.2011119 | Selected Topics in Signal Processing, IEEE Journal of |
Keywords | Field | DocType |
biomedical optical imaging,cancer,fuzzy logic,gradient methods,image segmentation,medical image processing,skin,EM level set,adaptive snake,adaptive thresholding,automatic method,dermoscopy images,fuzzy based split-and-merge algorithm,gradient vector flow,image segmentation,level set method,melanoma diagnosis,segmentation method comparison,semisupervised methods,skin lesion segmentation,Dermoscopy,melanoma,segmentation,skin lesion | Computer vision,Pattern recognition,Segmentation,Level set method,Medical imaging,Computer science,Level set,Image segmentation,Ground truth,Vector flow,Artificial intelligence,Thresholding | Journal |
Volume | Issue | ISSN |
3 | 1 | 1932-4553 |
Citations | PageRank | References |
85 | 3.76 | 13 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Margarida Silveira | 1 | 109 | 10.48 |
| Jacinto C. Nascimento | 2 | 396 | 40.94 |
| Jorge S. Marques | 3 | 535 | 67.78 |
| AndrÉ R. S. Marcal | 4 | 97 | 5.59 |
| Teresa Mendonca | 5 | 85 | 4.09 |
| Syogo Yamauchi | 6 | 85 | 3.76 |
| J. Maeda | 7 | 290 | 21.43 |
| Jorge Rozeira | 8 | 132 | 7.44 |