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.
This research is supported in part by an NIST grant #70NANB5H1183 and in part by the NIH/NCI research contract # N01-CO-07119.
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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. https://doi.org/10.1007/3-540-45468-3_79
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DOI: https://doi.org/10.1007/3-540-45468-3_79
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