Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evaluation of shape similarity measurement methods for spine X-ray images
Journal of Visual Communication and Image Representation
Localizing contour points for indexing an X-ray image retrieval system
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
A Spine X-Ray Image Retrieval System Using Partial Shape Matching
IEEE Transactions on Information Technology in Biomedicine
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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The objective of the proposed approach is to narrow down the semantic gap between the query and retrieval primitives in a spine image retrieval system. The proposed retrieval technique is based on a geometric eight-point model to enable formation of semantic query. The geometric eight-point model is uniform for all vertebrae, not biased by human experts and thus free from ambiguity. The features are extracted after automatically locating contour points of eight-point geometric model of spine vertebra. Then, vertebra is represented in feature space using region-based shape features, indicative of pathology (at two anterior corners). The proposed retrieval scheme uses Euclidean distance as similarity measure in feature space. It yields better result than an existing whole shape-based matching method for retrieval of spine images that uses Procrustes metric, in term of accuracy and parsimony of model. The approach is simple and computationally efficient.