Extracting morphological high-level intuitive features (HLIF) for enhancing skin lesion classification.
Feature extraction of skin lesions is necessary to provide automated tools for the detection of skin cancer. High-level intuitive features (HLIF) that measure border irregularity of skin lesion images obtained with standard cameras are presented. Existing feature sets have defined many low-level unintuitive features. Incorporating HLIFs into a set of low-level features gives more semantic meaning to the feature set, and allows the system to provide intuitive rationale for the classification decision. Promising experimental results show that adding a small set of HLIFs to the large state-of-the-art low-level skin lesion feature set increases sensitivity, specificity, and accuracy, while decreasing the cross-validation error.
Department of Systems Design Engineering, University of Waterloo, Ontario N2L 3G1, Canada. framelard@uwaterloo.ca
Robert
R
Amelard
Alexander
A
Wong
David A
DA
Clausi
D000465:Algorithms; D046169:Dermoscopy / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D009506:Nevus / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*