Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

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-...

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Bibliographic Details
Published in:IEEE Journal of Selected Topics in Signal Processing Vol. 3; no. 1; pp. 35 - 45
Main Authors: Silveira, M., Nascimento, J.C., Marques, J.S., Marcal, A.R.S., Mendonca, T., Yamauchi, S., Maeda, J., Rozeira, J.
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2009
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Algorithms
Dermoscopy
Feature extraction
Ground truth
Hair
Image color analysis
Image segmentation
Lesions
Level set
Malignant tumors
Mathematical analysis
melanoma
Reflection
Segmentation
Shape
Skin cancer
skin lesion
State of the art
Vectors (mathematics)
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary: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.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2008.2011119