Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification

Dermoscopy image as a non-invasive diagnosis technique plays an important role for early diagnosis of malignant melanoma. Even for experienced dermatologists, however, diagnosis by human vision can be subjective, inaccurate and non-reproducible. This is attributed to the challenging image characteristics including varying lesion sizes and their shapes, fuzzy lesion boundaries, different skin color types and presence of hair. To aid in the image interpretation, automatic classification of dermoscopy images have been shown to be a valuable aid in the clinical decision making. Existing methods however have problems in representing and differentiating skin lesions due to high degree of similarities between melanoma and non-melanoma images and large variations inherited from skin lesion images. To overcome these limitations, this study proposes a new automatic melanoma detection method for dermoscopy images via multi-scale lesion-biased representation (MLR) and joint reverse classification (JRC). Our proposed MLR representation enable us to represent skin lesions using multiple closely related histograms derived from different rotations and scales while traditional methods can only represent skin lesion using a single-scale histogram. The MLR representation was then used with JRC for melanoma detection. The proposed JRC model allows us to use a set of closely related histograms to derive additional information for melanoma detection, where existing methods mainly rely on histogram itself. Our method was evaluated on a public dataset of dermoscopy images, and we demonstrate superior classification performance compared to the current state-of-the-art methods.