Abstract. Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.
The US (USP), British (BP), European (EP) and Japanese (JP) pharmacopeias demand that [(18)F]FDG for injection should be clear and particulate free within the given shelf-life/expiration time. To avoid Al-phosphate precipitation within the product expiry time, FASTlab citrate cassettes, rather than phosphate cassettes, should be used for [(18)F]FDG production. Although testing for Al(3+) is not listed in the [(18)F]FDG monographs of the USP, BP and EP, residual Al(3+) levels should be considered in the interests of patient safety.
Possessing unique characteristics and demonstrating exceptional performances, the proposed CUDA-based bilateral filter adequately removed random noise in multifarious brain MR images for further study in neurosciences and radiological sciences. It requires no prior knowledge of the noise variance and automatically restores MR images while preserving fine details. The strategy of exploiting the CUDA to accelerate the computation and incorporating texture features into the BPN to completely automate the bilateral filtering process is achievable and validated, from which the best performance is reached.
Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. Most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. This paper attempts to systematically investigate significant attributes from popular image features and textures to facilitate subsequent automation process. In our approach, a total number of 39 image attributes are considered that are based on three categories: 1) Image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Tamura texture features. To obtain the ranking of discrimination in these texture features, a T-test is applied to each individual image features computed in every image based on noise levels, intensity distributions, and anatomical geometries. Preliminary results indicated that the order of significance in the texture features approximately varies in noise, slice, and normality. For distinguishing between noise levels, the features of contrast, standard deviation, angular second moment, and entropy from the GLCM class performed best. For distinguishing between slice positions, the features of mean and variance from the basic statistics class and the coarseness feature from the Tamuraclass outperformed other features.
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