Skin lesion image classification using sparse representation in quaternion wavelet domain

Automated melanoma classification remains a challenging task because skin lesion images are prone to low contrast and many kinds of artifacts. To handle these challenges, we introduce a novel and efficient method for skin lesion classification based on the machine learning approach and sparse representation (SR) in the quaternion wavelet (QW) domain. Further, we investigate the application of the SR approach with low, high, and mixed wavelet frequencies. Using QW coefficients, the classification problem is mapped onto the algebra of quaternions. Using the public skin lesion image datasets ISIC2017 and ISIC2019, we experimentally validated that creating dictionary with quaternions of low-frequency wavelet sub-band leads to the most accurate classification of skin lesions to melanoma or benign. We compared our approach with contemporary methods including neural networks.