Journal
NEUROCOMPUTING
Volume 101, Issue -, Pages 252-257Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2012.08.008
Keywords
Intervertebral disk degeneration; Shape; Texture; Active learning; Computer aided diagnosis
Categories
Funding
- Natural Science Foundation of China [61174170, 61004103]
- National Research Foundation for the Doctoral Program of Higher Education of China [20100111110005]
- Natural Science Foundation of Anhui Province of China [11040606Q44]
- China Postdoctoral Science Foundation [20090460742]
- Key Projects of Natural Science Research for Collages of Anhui Province [KJ2010A193]
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We consider the problem of the computer aided diagnosis on intervertebral disk degeneration in this paper, where a framework of classifying the disks into healthy and degenerated groups is proposed. First, we propose to separately model the shape and texture similarities and combine them into a single measure to describe the difference of imaging appearance between healthy and degenerated disks. Then we introduce the active learning strategy into our framework, aiming at training a well performed classifier with only a subset of data that are most representative. This would save much human effort by avoiding labeling all training data manually. Herein we propose a simple but effective improved transductive experimental design, taking advantage of the latent structure of the data, to achieve the automatic data selection. Experiments on synthetic data and real disc MRI show the effectiveness of our approach. (C) 2012 Elsevier B.V. All rights reserved.
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