Abstract:A discriminative dictionary representation method was developed to mitigate CT truncation artifacts directly in the DICOM image domain. Both phantom and human subject studies demonstrated that the proposed method can effectively reduce truncation artifacts without access to projection data.
“…Furthermore, our suepervoxel algorithm makes the size of supervoxel as regular as possible. The proposed supervoxel technique has high potential to be applied for the MRI images of other organs or other medical images, such as computed tomography and ultrasound imaging [ 36 , 37 ]. Fig.…”
BackgroundAccurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.MethodsTo overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals.ResultsThe performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset.ConclusionsThe proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.
“…Furthermore, our suepervoxel algorithm makes the size of supervoxel as regular as possible. The proposed supervoxel technique has high potential to be applied for the MRI images of other organs or other medical images, such as computed tomography and ultrasound imaging [ 36 , 37 ]. Fig.…”
BackgroundAccurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.MethodsTo overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals.ResultsThe performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset.ConclusionsThe proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.
“…Since the spectral CT image can be sparsely represented in each of its multiple energy channels, and the channels are highly correlated, the TDL method can produce superior image quality and leads to more accurate material decomposition. Chen [14] develops a platformindependent discriminative dictionary representation method to mitigate and reduce CT truncation artifacts directly in image domain. This discriminative dictionary is composed of an artifact sub dictionary and a non-artifact sub dictionary, which were constructed to achieve selective and sparse representation of artifact and non-artifact components of a CT image respectively.…”
Section: Introductionmentioning
confidence: 99%
“…j = (m-1) × W + n, m=1,…,H, n=1,…,W, H and W are respectively the width and height of the image x ; is a modulation parameter, which determines approximation degree.Finally, the output of this iteration will be obtained by the formula(14) …”
The iterative algorithms of computed tomography (CT) reconstruction derived from the dictionary learning (DL) regularization have been developed to make high quality recovery from the undersampled data acquired by a low dose protocol. However, when they are applied to noisy data with low sampling rate, streaking artifacts and bias tends to appear in early iteration results. Since the dictionary is over-complete, the artifacts and bias can also be represented well by the dictionary, resulting in the reservation of these unexpected structures in the final image. We proposed a smoothed L0 norm-constraint dictionary learning (SL0-DL) algorithm to deal with these unexpected structures. For the proposed algorithm, we introduce smoothed-L0 norm regularization to the objective function. In each iteration process, the intermediate image generated by the DL representation will be smoothed using SL0 norm, and then the smoothed image is used to update the output of this iteration. The raw data from both the numerical simulation and actual CT acquisition are used to test the performances of the proposed SL0-DL method. Experimental results demonstrate that the proposed method performs better than other competing algorithms with better noise and artifacts suppression performance while preserving image texture details. And the results show a significant improvement in the quality of the reconstructed image, which demonstrates that the proposed algorithm is really effective. INDEX TERMS Computer tomography (CT), low dose, streaking artifacts and bias, smoothed L 0 normconstraint dictionary learning (SL0-DL).
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