Information Retrieval

A Query-by-Example Content-Based Image Retrieval System of Non-Melanoma Skin Lesions
Lucia Ballerini1 , Xiang Li1 , Robert B. Fisher1 , and Jonathan Rees2
School of Informatics, University of Edinburgh, UK [e-mail], [e-mail], [e-mail] 2 Dermatology, University of Edinburgh, UK [e-mail]
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Abstract. This paper proposes a content-based image retrieval system for skin lesion images as a diagnostic aid. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types are used. Colour and texture features are extracted from lesions. Feature selection is achieved by optimising a similarity matching function. Experiments on our database of 208 images are performed and results evaluated.

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Introduction

Research in content-based image retrieval (CBIR) today is an extremely active discipline. There are already review articles containing references to a large number of systems and description of the technology implemented [1, 2]. A more recent review [3] reports a tremendous growth in publications on this topic. Applications of CBIR systems to medical domains already exist [4], although most of the systems currently available are based on radiological images. Most of the work in dermatology has focused on skin cancer detection. Different techniques for segmentation, feature extraction and classification have been reported by several authors. Concerning segmentation, Celebi et al. [5] presented a systematic overview of recent border detection methods: clustering followed by active contours are the most popular. Numerous features have been extracted from skin images, including shape, colour, texture and border properties [6–8]. Classification methods range from discriminant analysis to neural networks and support vector machines [9–11]. These methods are mainly developed for

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