Active learning based intervertebral disk classification combining shape and texture similarities

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• 作者：Shijie Hao^a ; ^{hfut.hsj@gmail.com}Author Vitae ; Jianguo Jiang^a ; ^bAuthor Vitae ; Yanrong Guo^aAuthor Vitae ; Hong Li^cAuthor Vitae
• 关键词：Intervertebral disk degeneration ; Shape ; Texture ; Active learning ; Computer aided diagnosis
• 刊名：Neurocomputing
• 出版年：2013
• 出版时间：4 February, 2013
• 年：2013
• 卷：101
• 期：Complete
• 页码：252-257
• 全文大小：535 K

文摘

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.