Abstract：In the existing 3D active appearance model (AAM), a 3D appearance matrix must be transformed into a 1D vector. Thus, the segmentation accuracy is affected by the transformation and the segmentation efficiency is affected by the generated oversize vector. For the above problems, a 3D tensor based AAM is proposed aiming at direct operating on 3D appearances of lungs by higher order singular value decomposition, rather than transforming it from 3D to 1D. Firstly, the tensor-based model is built and the parameters are deduced. Then, a block-based Kronecker is designed to determine the optimal scheme for low rank representation of appearance tensors. This optimization avoids repeated computation. Finally, the whole segmentation system is constructed and used in the segmentation of lung images. Experimental results of clinical CT images are compared to other 3D landmark-based methods. The proposed model performs a better result in both precision and efficiency.
王青竹，孙娜，王斌. 张量模式三维主动外观模型及其在肺CT图像分割中的应用*[J]. 模式识别与人工智能, 2015, 28(8): 750-759.
WANG Qing-Zhu, SUN Na , WANG Bin. Three-Dimensional Active Appearance Model Based on Tensor Mode andIts Application in Lung Field Segmentation from CT Images. , 2015, 28(8): 750-759.
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