Rubbings of China is a great treasure, of which the recorded information is later generations research was one of the important ways in politics, economy and culture. But the rubbings are vulnerable to damage. However, the partition of rubbings can make this information better preserved, which is of great significance to the protection of cultural relics, historical research and cultural inheritance. This paper combined region growing segmentation and connected domain labeling to segment the historical document image. Firstly, the OTSU algorithm has been employed for document image binarization. Second, the binarized image has been corroded to remove isolated noise points, and then the effective connected area is extracted by the connectivity domain marking algorithm to find the initial value seed point. Finally, the seed point segmented the area growth to obtain a clear binarized document image. Experiments showed that the automatic seed region growth leads to performance improvements over the K-means cluster and OSTU method. We tested our method using the DIBCO 2010 dataset, which showed segmentation accuracy of up to 98%.
Fluorescence molecular tomography (FMT) has been widely used in preclinical tumor imaging, which enables three-dimensional imaging of the distribution of fluorescent probes in small animal bodies via image reconstruction method. However, the reconstruction results are usually unsatisfactory in the term of robustness and efficiency because of the ill-posed and ill-conditioned of FMT problem. In this study, an FMT reconstruction method based on primal accelerated proximal gradient (PAPG) descent and L1-norm regularized projection (L1RP) is proposed. The proposed method utilizes the current and previous iterations to obtain a search point at each iteration. To achieve fast convergence, the PAPG method is applied to efficiently solve the search point, and then L1RP is performed to obtain the robust and accurate reconstruction. To verify the performance of the proposed method, simulation experiments are conducted. The comparative results revealed that it held advantages of robustness, accuracy, and efficiency in FMT reconstructions. Furthermore, a phantom experiment and an in vivo mouse experiment were also performed, which proved the potential and feasibility of the proposed method for practical applications.
Fluorescence molecular tomography (FMT) is developing rapidly in the field of molecular imaging. FMT has been
used in surgical navigation for tumor resection and has many potential applications at the physiological, metabolic, and
molecular levels in tissues. Due to the ill-posed nature of the problem, many regularized methods are generally adopted.
In this paper, we propose a region reconstruction method for FMT in which the trace norm regularization. The trace
norm penalty was defined as the sum of the singular values of the matrix. The proposed method adopts a priori
information which is the structured sparsity of the fluorescent regions for FMT reconstruction. In order to improve the
solution efficiency, the accelerated proximal gradient algorithms was used to accelerate the computation. The
numerical phantom experiment was conducted to evaluate the performance of the proposed trace norm regularization
method. The simulation study shows that the proposed method achieves accurate and is able to reconstruct image
effectively.
Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in small animals. FMT has been used in surgical navigation for tumor resection and has many potential applications at the physiological, metabolic, and molecular levels in tissues. The hybrid system combined FMT and X-ray computed tomography (XCT) was pursued for accurate detection. However, the result is usually over-smoothed and over-shrunk. In this paper, we propose a region reconstruction method for FMT in which the elastic net (E-net) regularization is used to combine L1-norm and L2-norm. The E-net penalty corresponds to adding the L1-norm penalty and a L2-norm penalty. Elastic net combines the advantages of L1-norm regularization and L2-norm regularization. It could achieve the balance between the sparsity and smooth by simultaneously employing the L1-norm and the L2-norm. To solve the problem effectively, the proximal gradient algorithms was used to accelerate the computation. To evaluate the performance of the proposed E-net method, numerical phantom experiments are conducted. The simulation study shows that the proposed method achieves accurate and is able to reconstruct image effectively.
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