Proc. SPIE. 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016)
KEYWORDS: Super resolution, Magnetic resonance imaging, Image processing, Image restoration, Image resolution, Medical imaging, Associative arrays, 3D image processing, Lawrencium, 3D magnetic resonance imaging
Clinical practice requires multiple scans with different modalities for diagnostic tasks, but each scan does not produce the image of the same resolution. Such phenomenon may influence the subsequent analysis such as registration or multimodal segmentation. Therefore, performing super-resolution (SR) on clinical images is needed. In this paper, we present a unified SR framework which takes advantages of two primary SR approaches – self-learning SR and learning-based SR. Through the self-learning SR process, we succeed in obtaining a second-order approximation of the mapping functions between low and high resolution image patches, by leveraging a local regression model and multi-scale self-similarity. Through the learning-based SR process, such patch relations are further refined by using the information from a reference HR image. Extensive experiments on open-access MRI images have validated the effectiveness of the proposed method. Compared to other advanced SR approaches, the proposed method provides more realistic HR images with sharp edges.
Denoising is the primary preprocessing step before subsequent clinical diagnostic analysis of MRI data. Common patch-based denoising methods rely heavily on the degree of patch matching, which limits their performance by the necessity of finding sufficiently similar patches. In this paper, we propose a global filtering framework, in which each voxel is restored with information from the whole 3D image. This global filter is not restricted to any specific patchbased filter, as it is a low-rank approximation using the Nyström method combined with a low sampling rate and a kmeans clustering adaptive sampling scheme. Experiments demonstrate that this method utilizes information effectively from the whole image for denoising, and the framework can be applied on top of most patch-based methods to further improve the performance.
Motion blur due to camera shaking during exposure is one common phenomena of image degradation. Image motion deblurring is an ill-posed problem, so regularization with image prior and (or) PSF prior is used to estimate PSF and (or) recover original image. In this paper, we exploit image edge prior to estimate PSF based on useful edge selection rule. And we still adopt L1 norm of PSF to ensure its sparsity and Tikhonov regularization to ensure its smoothing during the PSF estimation procedure. And the Laplacian image prior is adopted to restore latent image. The experiment shows that the proposed algorithm outperforms other algorithms.
To investigate the relation between biosensor of endotoxin and endotoxin of plasma in sepsis. Method: biosensor of endotoxin was designed with technology of quartz crystal microbalance bioaffinity sensor ligand of endotoxin were immobilized by protein A conjugate. When a sample soliton of plasma containing endotoxin 0.01, 0.03, 0.06, 0.1, 0.5, 1.0Eu, treated with perchloric acid and injected into slot of quartz crystal surface respectively, the ligand was released from the surface of quartz crystal to form a more stable complex with endotoxin in solution. The endotoxin concentration corresponded to the weight change on the crystal surface, and caused change of frequency that occurred when desorbed. The result was biosensor of endotoxin might detect endotoxin of plasma in sepsis, measurements range between 0.05Eu and 0.5Eu in the stop flow mode, measurement range between 0.1Eu and 1Eu in the flow mode. The sensor of endotoxin could detect the endotoxin of plasm rapidly, and use for detection sepsis in clinically.