Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging
スポンサーリンク
概要
- 論文の詳細を見る
Surface integrity of 3D medical data is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridge the gap, hole-filling for medical imaging is a popular research topic in recent years[1]-[3]. Considering that a medical image, e.g. CT or MRI, has the natural form of a tensor, we recognize the problem of medical hole-filling as the extension of Principal Component Pursuit (PCP) problem from matrix case to tensor case. Since the new problem in the tensor case is much more difficult than the matrix case, an efficient algorithm for the extension is presented by relaxation technique. The most significant feature of our algorithm is that unlike traditional methods which follow a strictly local approach, our method fixes the hole by the global structure in the specific medical data. Another important difference from the previous algorithm [4] is that our algorithm is able to automatically separate the completed data from the hole in an implicit manner. Our experiments demonstrate that the proposed method can lead to satisfactory results.
著者
-
GUO Lv
IPRAI, Shanghai Jiao Tong University
-
LI Yin
IPRAI, Shanghai Jiao Tong University
-
YANG Jie
IPRAI, Shanghai Jiao Tong University
-
LU Li
IPRAI, Shanghai Jiao Tong University
-
Yang Jie
Iprai Shanghai Jiao Tong University
-
Lu Li
Iprai Shanghai Jiao Tong University
-
Li Yin
Iprai Shanghai Jiao Tong University
-
Guo Lv
Iprai Shanghai Jiao Tong University
関連論文
- Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging
- Exploration into Single Image Super-Resolution via Self Similarity by Sparse Representation