A shape-based approach for image retrieval in healthcare information infrastructure
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Date
2008
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Zululand
Abstract
This study investigated some models and techniques that would help build
shape-based image retrieval with an improved accuracy. As an initial step, a
modular prototype system, called BrainSearch was implemented and used to
demonstrate the utility of our algorithms and techniques on brain Magnetic
Resonance Imaging (MR1) characterization and their suitability for image
retrieval. The system supports retrieval based on shape similarity, a single
keyword image annotation and five brain MRl classes. The BrainSearch
system was realized to make it easy to test retrieval performance and to
expedite further algorithm investigation. This was made possible by the
implementation of region-based Local Binary Fitting (LBF) active contour,
Density histogram of Feature Points (DFP) shape representation and k-Nearest
Neighbor (k-NN) classifier.
Then we performed a series of experiments to evaluate the
performance of BrainSearch utilizing different retrieval techniques. Results
generally showed that (a) region-based DFP shape representation is better than
edge-based DFP shape representation, whether pre-classification of images is
used or not, (b) retrieval technique that uses pre-classification of images gives
better results than retrieval technique that uses non-classification of images, no
matter the DFP shape representation used, (c) the pre-classification of images
cannot improve edge-based DFP shape representation better than when region based
DFP alone is used, and (d) the pre-classification of images as well as
factors, like shape representations and similarity measures, improve retrieval
performance of BrainSearch system. Overall, the hybrid combination of LBF
active contour, DFP shape representation and k-NN classifier is promising for
the retrieval ofbrain MRI.
Description
Submitted to the Faculty of Science in the Department of Computer Science at the University of Zululand, 2008.
Keywords
Image retrieval, Shape-based image retrieval