Please use this identifier to cite or link to this item: https://doi.org/10.3233/978-1-61499-289-9-739
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
Source: 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. https://doi.org/10.3233/978-1-61499-289-9-739
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
URI: http://scholarbank.nus.edu.sg/handle/10635/78414
ISBN: 9781614992882
ISSN: 09269630
DOI: 10.3233/978-1-61499-289-9-739
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

35
checked on Feb 19, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.