Border detection in dermoscopy images using statistical region merging.
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).


CONCLUSIONS
The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.
Celebi
M Emre
ME
Department of Computer Science, Louisiana State University in Shreveport, Shreveport, LA 71115, USA. ecelebi@lsus.edu
Kingravi
Hassan A
HA
Iyatomi
Hitoshi
H
Aslandogan
Y Alp
YA
Stoecker
William V
WV
Moss
Randy H
RH
Malters
Joseph M
JM
Grichnik
James M
JM
Marghoob
Ashfaq A
AA
Rabinovitz
Harold S
HS
Menzies
Scott W
SW
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000473:pathology*