Classification of melanoma based on feature similarity measurement for codebook learning in the bag-of-features model

Bag-of-features (BoF) model based melanoma classification methods can effectively assist dermatologists to diagnose skin diseases. Codebook learning is a key step in the BoF model and the k-means clustering algorithm is often used to learn a codebook. However, the cluster centers generated by k-means algorithm are irresistibly attracted to the denser regions. This produces a suboptimal codebook in which most of the clusters are located in dense regions and a few are in sparse regions. Therefore, this can easily affect the classification accuracy. In this paper, we develop a novel methodology for classifying skin lesions. Firstly, we propose a new codebook learning algorithm based on feature similarity measurement (FSM) to effectively quantify the original features of melanomas. We utilize the combination of the linearly independent and linear prediction (LP) algorithms to measure feature similarity. Especially, the code-words learned by the proposed FSM algorithm are not affected by the density of samples. Therefore, a more discriminating BoF histogram for the melanoma classification is achieved. Secondly, we propose a melanoma classification method based on the FSM codebook learning algorithm. In particular, we adopt the BoF histogram fusion strategy of different feature descriptors, i.e., RGB color histogram and scale-invariant feature transform (SIFT). Finally, the experimental results show that the proposed melanoma classification method outperforms some state-of-the-art methods in terms of classification accuracy and efficiency. The results also show the performance of the proposed method is greatly improved by the use of the proposed codebook learning algorithm. (C) 2019 Elsevier Ltd. All rights reserved.