Unsupervised medical image classification by combining case-based classifiers

• Main Authors: Dinh, Thien Anh, Silander, Tomi, Su, Bolan, Gong, Tianxia, Pang, Boon Chuan, Lim, C. C. Tchoyoson, Lee, Cheng Kiang, Tan, Chew Lim, Tze-Yun LEONG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Classification; Image processing; Medical images; Traumatic brain injury; Computer Sciences; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3052
• Institution: Singapore Management University

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.