CAJ | 학술논문

感兴趣区域( ROI)的分类是医学图像的计算机辅助诊断过程的最后一步，传统方法只针对每个ROI区域单独提取特征，再利用统计学习的方法训练分类器进行分类。然而图像中每个区域所包含的视觉特征有限，很难进行准确的分类。文中提出一种基于LDA主题模型的改进模型( LDAC)，考虑ROI周围区域，即图像的上下文关系，通过利用LDA对ROI周围区域所包含的上下文信息进行建模，同时结合ROI区域的视觉信息和类别标签，从而辅助ROI区域的分类，以达到提高分类准确率的目的。乳腺图像肿块分类实验表明，文中方法可提高分类的准确性。
감흥취구역( ROI)적분류시의학도상적계산궤보조진단과정적최후일보，전통방법지침대매개ROI구역단독제취특정，재이용통계학습적방법훈련분류기진행분류。연이도상중매개구역소포함적시각특정유한，흔난진행준학적분류。문중제출일충기우LDA주제모형적개진모형( LDAC)，고필ROI주위구역，즉도상적상하문관계，통과이용LDA대ROI주위구역소포함적상하문신식진행건모，동시결합ROI구역적시각신식화유별표첨，종이보조ROI구역적분류，이체도제고분류준학솔적목적。유선도상종괴분류실험표명，문중방법가제고분류적준학성。
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.