Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images.
In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel "edge object value (EOV) threshold" method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA. hwqkc@mst.edu
Hanzheng
H
Wang
Randy H
RH
Moss
Xiaohe
X
Chen
R Joe
RJ
Stanley
William V
WV
Stoecker
M Emre
ME
Celebi
Joseph M
JM
Malters
James M
JM
Grichnik
Ashfaq A
AA
Marghoob
Harold S
HS
Rabinovitz
Scott W
SW
Menzies
Thomas M
TM
Szalapski
D000465:Algorithms; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*