Unsupervised medical image classification by combining case-based classifiers

Publication Type

Conference Proceeding Article

Publication Date

12-2013

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.

Keywords

Classification, Image processing, Medical images, Traumatic brain injury

Discipline

Computer Sciences | Health Information Technology

Publication

MEDINFO 2013: Proceeding of the 14th World Congress on Medical and Health Informatics

Volume

192

First Page

739

Last Page

743

ISBN

9781614992882

Identifier

10.3233/978-1-61499-289-9-739

Publisher

IOS Press

City or Country

Amsterdam, The Netherlands

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