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Neurocomputing v.229, 2017년, pp.3 - 12   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images

Zhang, Lichi (Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, China ) ; Wang, Qian (Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, China ) ; Gao, Yaozong (Department of Radiology and BRIC, University of North Carolina at Chapel Hill, United States ) ; Li, Hongxin (Department of Neonatology, The Affiliated Changzhou Children's Hospital of Nantong University, Changzhou, Jiangsu 213003, China ) ; Wu, Guorong (Department of Radiology and BRIC, University of North Carolina at Chapel Hill, United States ) ; Shen, Dinggang (Department of Radiology and BRIC, University of North Carolina at Chapel Hill, United States ) ;
  • 초록  

    Abstract Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First , each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second , a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore , we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.


  • 주제어

    Image segmentation .   Random forest .   Brain MR images .   Atlas selection .   Clustering.  

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