|Title||Extracting High-Level Intuitive Features (HLIF) For Classifying Skin Lesions Using Standard Camera Images|
|Publication Type||Conference Paper|
|Year of Publication||2012|
|Authors||Amelard, R., A. Wong, and D. A. Clausi|
|Conference Name||9th Conference on Computer and Robot Vision|
|Keywords||decision support, feature descriptor, Feature Extraction, Melanoma, skin lesion|
High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are presented. These features can be used to help dermatologists objectively diagnose lesions as cancerous (melanoma) or benign with intuitive rationale. Existing work defines large sets of low-level statistical features for analysing skin lesions. The proposed HLIFs are designed such that smaller sets of HLIFs can capture more deterministic information than large sets of low-level features. Analytical reasoning is given for each feature to show how it aptly describes asymmetry. Promising experimental results show that classification using the proposed HLIF set, although only one-tenth the size of the existing state-of-the-art low-level feature set, labels the data with better or comparable success. The best classification is obtained by combining the low-level feature set with the HLIF set.