Open AccessBorder detection in dermoscopy images using statistical region merging
Author(s) -
Emre Celebi M.,
Kingravi Hassan A.,
Iyatomi Hitoshi,
Alp Aslandogan Y.,
Stoecker William V.,
Moss Randy H.,
Malters Joseph M.,
Grichnik James M.,
Marghoob Ashfaq A.,
Rabinovitz Harold S.,
Menzies Scott W.
Publication year - 2008
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2008.00301.x
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , cluster analysis , metric (unit) , ground truth , set (abstract data type) , computer vision , operations management , economics , programming language
Background: 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, because the accuracy of the subsequent steps crucially depends on it. Methods: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm. Results: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist‐determined borders are used as the ground‐truth. The proposed method is compared with four state‐of‐the‐art automated methods (orientation‐sensitive fuzzy c‐means, dermatologist‐like tumor extraction algorithm, meanshift clustering, and the modified JSEG method). Conclusion: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.