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Classi cation-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures (2007)

Abstract
Abstract. This paper reports our methodology and initial results on volumetric pathological neuroimage retrieval. A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains. We apply memory-based learning method to nd the most-discriminative feature subset through image classication according to predened semantic categories. Finally, this selected feature subset is used as indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.

Publication details
Download http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.21.1660
Source http://www.cs.cmu.edu/afs/cs.cmu.edu/project/nist/ftp/MICCAI2001.ps.Z
Contributors CiteSeerX
Repository CiteSeerX - Scientific Literature Digital Library and Search Engine (United States)
Type text
Language English
Relation 10.1.1.85.1157, 10.1.1.113.717, 10.1.1.120.2241, 10.1.1.28.4680, 10.1.1.21.3099, 10.1.1.59.6948