Unsupervised Medical Image Classification by Combining Case-Based Classifiers
Dinh Thien Anh, Tomi Silander, Bolan Su, Tianxia Gong, Pang Boon Chuan, Lim C. C. Tchoyoson, Lee Cheng Kiang, Tan Chew Lim, Tze-Yun Leong · Studies in health technology and informatics · 2013
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.