Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24291
Title: Regression and classification based distance metric learning for medical image retrieval
Authors: Cai, W
Song, Y
Feng, DD
Keywords: Classification
Distance metric
Image retrieval
Regression
Sparsity
Issue Date: 2012
Publisher: IEEE
Source: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2-5 May 2012, Barcelona, p. 1775-1778 How to cite?
Journal: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2-5 May 2012, Barcelona 
Abstract: Better utilizing the vast amount of valuable information stored in the medical imaging databases is always an interesting research area, and one way is to retrieve similar images as a reference dataset to assist the diagnosis. Distance metric is a core component in image retrieval; and in this paper, we propose a new learning-based distance metric design, based on regression and classification techniques. We design a weight learning approach by classifying the similar-dissimilar data samples, and a further optimization with a sparsity-constraint regression algorithm for feature selection. The learned distance metric is generally applicable for medical image retrievals. We evaluate the proposed method on clinical PET-CT images, and demonstrate clear performance improvements.
URI: http://hdl.handle.net/10397/24291
ISBN: 978-1-4577-1857-1
ISSN: 1945-7928
DOI: 10.1109/ISBI.2012.6235925
Appears in Collections:Conference Paper

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