A Context Based ROI Classification Method in Medical Images
GUO Qiao-Jin, LI Ning, XIE Jun-Yuan
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 Department of Computer Science and Technology, Nanjing University, Nanjing 210023
Abstract：Region of interest (ROI) classification is the last and very important step in the process of computer-aided diagnosis with medical images. Traditional methods only employ local visual features of ROI for classification. Thus, the accurate classification can not be achieved under some circumstances. To improve the classification accuracy, the context information is extracted from regions around ROI. A latent Dirichlet allocation classification (LDAC) model based on LDA is proposed, which utilizes LDA to capture contextual information of ROI from surrounding regions. The proposed model is applied to mammograms and experimental results show that the classification accuracy is improved.
郭乔进，李宁，谢俊元. 一种基于上下文的医学图像ROI分类方法[J]. 模式识别与人工智能, 2014, 27(12): 1057-1064.
GUO Qiao-Jin, LI Ning, XIE Jun-Yuan. A Context Based ROI Classification Method in Medical Images. , 2014, 27(12): 1057-1064.
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2014, Vol. 27 
