Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging

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. Considering that medical image, e.g. CT or MRI, has the natural form of tensor, we recognize the problem of medical hole-filling as the extension of PCP problem from matrix case to tensor case. Since the new problem in tensor case is much more difficult than the matrix case, we design an efficient algorithm for the extension 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 to previous algorithm 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 result.