Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color.
BACKGROUND
Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area).


METHODS
Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch.


RESULTS
The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented.


CONCLUSIONS
Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.
The Dermatology Center, Rolla, MO 65401-4600, USA. wvs@umr.edu
William V
WV
Stoecker
Kapil
K
Gupta
R Joe
RJ
Stanley
Randy H
RH
Moss
Bijaya
B
Shrestha
D003116:Color; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000473:pathology*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012878:Skin Neoplasms / Q000473:pathology*