Providing physicians with a set of pathology-confirmed similar images to a new difficult case can efficiently assist towards a more confident diagnosis; this concept is called Content-Based Image Retrieval. We used SURF interest point detection to find and match similar dermoscopy images from a labeled dermoscopic image database. SURF automatically finds points of interest with the shape of blobs, dots. Haar - wavelet responses and local color histograms are locally extracted from each detected key point. The similarity of two images is decided by matching their key points and finding the Euclidean distance between them. We evaluated our system’s performance based on its ability for retrieving images with the same texture features and similar diagnosis. For query images containing a pigment network the precision with retrieval of 9 images, P(9), is 75%; for dots and globules, the precision P(9) is 80%. The precision P(9) for Melanoma diagnosis is 72%, which is acceptable forsuch systems.
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