Benam, Ardalan - Similar Image Retrieval for Dermoscopy Images Using Interest Point Detection...

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This thesis has been submitted to the Library for purposes of graduation, but needs to be audited for technical details related to publication in order to be approved for inclusion in the Library collection.
Term: 
Spring 2017
Degree: 
M.Sc.
Degree type: 
Thesis
Department: 
School of Computing Science
Faculty: 
Applied Sciences
Senior supervisor: 
Stella Atkins
Co-supervisor, if any: 
Mark Drew
Thesis title: 
Similar Image Retrieval for Dermoscopy Images Using Interest Point Detection
Given Names: 
Ardalan
Surname: 
Benam
Abstract: 
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 for such systems.
Keywords: 
Dermoscopy; Content-based Image Retrieval; CBIR; Image retrieval; Interest Point Detection
Total pages: 
50