Color quantization of dermoscopy images using the K-means clustering algorithm

Abstract

Color quantization (CQ) is an important operation with various applications in medical image analysis. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the CQ literature because of its high computational requirements and sensitivity to initialization. In this chapter, we investigate the performance of a recently proposed k-means based CQ method. Experiments on a diverse set of dermoscopy images of skin lesions demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.