Color Medical Image Analysis

Volume 6 of the series Lecture Notes in Computational Vision and Biomechanics pp 63-86

A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions

  • Lucia BalleriniAffiliated withSchool of Informatics, University of Edinburgh Email author 
  • , Robert B. FisherAffiliated withSchool of Informatics, University of Edinburgh
  • , Ben AldridgeAffiliated withDepartment of Dermatology, University of Edinburgh
  • , Jonathan ReesAffiliated withDepartment of Dermatology, University of Edinburgh

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This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. The accuracy of the proposed hierarchical scheme is higher than 93 % in discriminating cancer and potential at risk lesions from benign lesions, and it reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive known result on non-melanoma skin cancer classification using color and texture information from images acquired by a standard camera (non-dermoscopy).