|Title||Extracting Morphological High-Level Intuitive Features (HLIF) for Enhancing Skin Lesion Classification|
|Publication Type||Conference Paper|
|Year of Publication||2012|
|Authors||Amelard, R., A. Wong, and D. A. Clausi|
|Conference Name||34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society|
|Conference Location||San Diego|
Feature extraction of skin lesions is necessary to provide automated tools for the detection of skin cancer. Highlevel 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 lowlevel 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 lowlevel skin lesion feature set increases sensitivity, specificity, and accuracy, while decreasing the cross-validation error.