Multiple sclerosis: changes in microarchitecture of white matter tracts after training with a video game balance board

Purpose: To determine if high-intensity, task-oriented, visual feedback training with a video game balance board (Nintendo Wii) induces significant changes in diffusion-tensor imaging (DTI) parameters of cerebellar connections and other supratentorial associative bundles and if these changes are related to clinical improvement in patients with multiple sclerosis.Conclusion: Despite the low statistical power (35%) due to the small sample size, the results showed that training with the balance board system modified the microstructure of superior cerebellar peduncles. The clinical improvement observed after training might be mediated by enhanced myelinationrelated processes, suggesting that high-intensity, taskoriented exercises could induce favorable microstructural changes in the brains of patients with multiple sclerosis.Materials and Methods: The protocol was approved by local ethical committee; each participant provided written informed consent. In this 24-week, randomized, two-period crossover pilot study, 27 patients underwent static posturography and brain magnetic resonance (MR) imaging at study entry, after the first 12-week period, and at study termination. Thirteen patients started a 12-week training program followed by a 12-week period without any intervention, while 14 patients received the intervention in reverse order. Fifteen healthy subjects also underwent MR imaging once and underwent static posturography. Virtual dissection of white matter tracts was performed with streamline tractography; values of DTI parameters were then obtained for each dissected tract. Repeated measures analyses of variance were performed to evaluate whether DTI parameters significantly changed after intervention, with false discovery rate correction for multiple hypothesis testing.Results: There were relevant differences between patients and healthy control subjects in postural sway and DTI parameters (P <.05). Significant main effects of time by group interaction for fractional anisotropy and radial diffusivity of the left and right superior cerebellar peduncles were found (F2,23 range, 5.555-3.450; P = .036-.088 after false discovery rate correction). These changes correlated with objective measures of balance improvement detected at static posturography (r = 20.381 to 0.401, P < .05). However, both clinical and DTI changes did not persist beyond 12 weeks after training

Radial Basis Functions Interpolation and Applications: An Incremental Approach

Radial Basis Functions (RBF) interpolation is primarily used for interpolation of scattered data in higher dimensions. The RBF interpolation is a non-separable interpolation which offers a smooth interpolation, generally in n-dimensional space. We present a new method for RBF computation using an incremental approach. The proposed method is especially convenient in cases when larger data sets are randomly updated as the proposed method is of O(N2) computational complexity instead of O(N3) for insert / remove operations only and therefore it is much faster than the standard approach. If t-varying data or vector data are to be interpolated, the proposed method offers a significant speed-up as well

Error Concealment using Neural Networks for Block-Based Image Coding

In this paper, a novel adaptive error concealment (EC) algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison

Dictionary Learning-based Inpainting on Triangular Meshes

The problem of inpainting consists of filling missing or damaged regions in images and videos in such a way that the filling pattern does not produce artifacts that deviate from the original data. In addition to restoring the missing data, the inpainting technique can also be used to remove undesired objects. In this work, we address the problem of inpainting on surfaces through a new method based on dictionary learning and sparse coding. Our method learns the dictionary through the subdivision of the mesh into patches and rebuilds the mesh via a method of reconstruction inspired by the Non-local Means method on the computed sparse codes. One of the advantages of our method is that it is capable of filling the missing regions and simultaneously removes noise and enhances important features of the mesh. Moreover, the inpainting result is globally coherent as the representation based on the dictionaries captures all the geometric information in the transformed domain. We present two variations of the method: a direct one, in which the model is reconstructed and restored directly from the representation in the transformed domain and a second one, adaptive, in which the missing regions are recreated iteratively through the successive propagation of the sparse code computed in the hole boundaries, which guides the local reconstructions. The second method produces better results for large regions because the sparse codes of the patches are adapted according to the sparse codes of the boundary patches. Finally, we present and analyze experimental results that demonstrate the performance of our method compared to the literature

Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation

Watermarking-Based Inpainting Under Data Transmition Environment

[[abstract]]This studyb proposes a novel image inpainting technique based on watermarking and halftoning. This technique use LSB method to embed error diffusion halftone image into original image for protecting the image. In image repair process, we use LSB method to extract the halftone information, and the reference image is achieved from LUT inverse halftone. Finally we use the reference imageto finish the image inpainting work. Experiment shows the performance of our method is very excellent in image inpainting.[[conferencetype]]國際[[conferencedate]]20101206~20101208[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Chengdu, Chin

Image defect detection algorithm based on deep learning

In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects

BIOCONSTRUCTION IN TRAUMATOLOGY

In the pages of his book the author shares his experience of inventor and creator of the new system for the situated on bone stable - functional osteosynthesis "METOST". On the most parts of this system components the author received a patent for an invention. Over the past 20–30 years, the stable – functional osteosynthesis of long tubular bones is confidently taking the leading place, as well in our country as abroad. However, the fixatives that are used for this purpose are not ideal and need to be improved. Each step and every element of the METOST system has been subjected to comprehensive analysis, mechanical and mathematical modeling and testing on experimental animals. Only after that, the author started using METOST system in the clinic on patients and achieved excellent results, having operated over 1,500 patients and reducing the percentage of errors and complications to – 1.5 %. This work could be used as a handbook for master classes for creative doctors who have dedicated their efforts to improving the methods of patients treatment. The monograph could be useful for all operating traumatologists - orthopedists, as well experienced doctors, as young aimed to succes – inventors. Indexing: &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;In the pages of his book the author shares his experience of inventor and creator of the new system for the situated on bone stable - functional osteosynthesis "METOST". On the most parts of this system components the author received a patent for an invention. Over the past 20–30 years, the stable – functional osteosynthesis of long tubular bones is confidently taking the leading place, as well in our country as abroad. However, the fixatives that are used for this purpose are not ideal and need to be improved. Each step and every element of the METOST system has been subjected to comprehensive analysis, mechanical and mathematical modeling and testing on experimental animals. Only after that, the author started using METOST system in the clinic on patients and achieved excellent results, having operated over 1,500 patients and reducing the percentage of errors and complications to – 1.5 %. This work could be used as a handbook for master classes for creative doctors who have dedicated their efforts to improving the methods of patients treatment. The monograph could be useful for all operating traumatologists - orthopedists, as well experienced doctors, as young aimed to succes – inventors. Indexing: &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp; &nbsp;&nbsp