Fast and Accurate Border Detection in Dermoscopy Images using Statistical Region Merging
As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist- determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.
M. E. Celebi et al., "Fast and Accurate Border Detection in Dermoscopy Images using Statistical Region Merging," Proceedings of Medical Imaging 2007: Image Processing (2007, San Diego, CA), vol. 6512, no. Part 3, SPIE, Feb 2007.
The definitive version is available at https://doi.org/10.1117/12.709073
Medical Imaging 2007: Image Processing (2007: Feb. 18-20, San Diego, CA)
Electrical and Computer Engineering
Keywords and Phrases
Algorithms; Computer Aided Diagnosis; Dermatology; Image Processing; Optimization; Statistical Methods; Border Detection; Dermoscopy; Melanoma; Skin Lesion; Statistical Region Merging; Imaging Techniques; Segmentation
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Article - Conference proceedings
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