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Title: Unsupervised medical image classification by combining case-based classifiers
Authors: Dinh, T.A.
Silander, T.
Su, B.
Gong, T.
Pang, B.C.
Lim, C.C.T.
Lee, C.K.
Tan, C.L. 
Leong, T.-Y. 
Keywords: classification
image processing
Medical images
traumatic brain injury
Issue Date: 2013
Citation: Dinh, T.A., Silander, T., Su, B., Gong, T., Pang, B.C., Lim, C.C.T., Lee, C.K., Tan, C.L., Leong, T.-Y. (2013). Unsupervised medical image classification by combining case-based classifiers. Studies in Health Technology and Informatics 192 (1-2) : 739-743. ScholarBank@NUS Repository.
Abstract: We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients. © 2013 IMIA and IOS Press.
Source Title: Studies in Health Technology and Informatics
ISBN: 9781614992882
ISSN: 09269630
DOI: 10.3233/978-1-61499-289-9-739
Appears in Collections:Staff Publications

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