Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
Conference Paper, Proceedings of 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01), pp. 655 - 665, October, 2001
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 findt he most-discriminative feature subset through image classification according to predefined 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.
BibTeX
@conference{Liu-2001-8315,author = {Yanxi Liu and Frank Dellaert and William E. Rothfus and Andrew Moore and Jeff Schneider and Takeo Kanade},
title = {Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures},
booktitle = {Proceedings of 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01)},
year = {2001},
month = {October},
pages = {655 - 665},
address = {Utrecht, The Netherlands},
}
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