Fuzzy logic techniques for blotch feature evaluation in dermoscopy images.
Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.
Khan
Azmath
A
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409-0040, USA.
Gupta
Kapil
K
Stanley
R J
RJ
Stoecker
William V
WV
Moss
Randy H
RH
Argenziano
Giuseppe
G
Soyer
H Peter
HP
Rabinovitz
Harold S
HS
Cognetta
Armand B
AB
D003124:Colorimetry / Q000379:methods; D046169:Dermoscopy / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology*; D016571:Neural Networks, Computer; D009506:Nevus / Q000175:diagnosis* / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000473:pathology*