Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
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- Liu Y., Dellaert F., Rothfus W.E., Moore A., Schneider J., Kanade T. (2001) Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. In: Niessen W.J., Viergever M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg
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.
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