Melanoma recognition in dermoscopy images using lesion's peripheral region information

Melanoma is one of the most dangerous forms of skin cancer, but it has a high survival rate if diagnosed on time. The first diagnostic approach in melanoma recognition is to visually assess the lesion through dermoscopic images. Computer-aided diagnosis systems for melanoma recognition has attracted a lot of attention in the last decade and proved to be helpful in that area. Methods for skin lesions analysis usually involves three main steps: lesion segmentation, feature extraction, and features classification. Extracting highly discriminative features from the lesion has a great impact on the recognition task. In this paper, we are seeking a lesion recognition system that incorporates these highly discriminative features.

For segmentation step, we use contour propagation model with a novel two-component speed function. In the feature extraction step, a new set of features based on peripheral information of the lesion are introduced. For this end, the peripheral area of the lesion is mapped to log-polar space using the Daugman's transformation and then a set of texture features are extracted from it. Newly introduced features do not need further segmentation of dermoscopic structures and are robust against lesion's scale, orientation, location, and shape variation. We also design the other global texture features to describe only the information from the lesion area. In the classification step, we evaluated two different schemes to prove the distinction power of the new features, one comprises linear SVM to recognize melanoma vs. nevus and the other scheme uses RUSBoost classifier to recognize melanoma vs. nevus and atypical-nevus. Sequential feature selection algorithm has been utilized in each classification scheme to rank features based on their distinction power.

Cross-validation experiments on the well-known PH² dataset resulted in an average of 97% for sensitivity and 100% for specificity on melanoma vs. nevus recognition task using only four features. Also, in the second classification scheme, we achieved high sensitivity and specificity values of 95% for melanoma vs. nevus and atypical nevus recognition experiments.

High values for evaluation metrics show that the proposed melanoma recognition system is superior to the other state-of-the-art algorithms, which proves the high distinction power of the newly introduced features.