Unsupervised medical image classification by combining case-based classifiers
Conference Proceeding Article
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.
Classification, Image processing, Medical images, Traumatic brain injury
Computer Sciences | Health Information Technology
Data Management and Analytics
MEDINFO 2013: Proceeding of the 14th World Congress on Medical and Health Informatics
City or Country
Amsterdam, The Netherlands
Dinh, Thien Anh; Silander, Tomi; Su, Bolan; Gong, Tianxia; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tan, Chew Lim; and Tze-Yun LEONG.
Unsupervised medical image classification by combining case-based classifiers. (2013). MEDINFO 2013: Proceeding of the 14th World Congress on Medical and Health Informatics. 192, 739-743. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3052
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