Adaptive algorithms play an important role in order to improve performance of diffusion distributed network. In comparison of diffusion Normalized least mean square algorithm, family of diffusion subband algorithms have faster convergence rate when the input signal is highly correlated. This paper solves the problem of distributed estimation in the diffusion networks based on improved multiband-structured subband adaptive filter (IMSAF) and diffusion improved multiband structured subband adaptive filter (DIMSAF) is established. In proposed algorithm, convergence behavior improved due to using several input projections instead of single vector of input data. In addition, when the projection order is increased, the convergence rate of the proposed algorithm improves. The validity of the DIMSAF in comparison of DLMS, DAPA, VSS-DAPA and DRLS algorithms is demonstrated by several computer simulations. The results show fastest convergence rate.

Image prosseing methods is useful for extraction of the defects such as porosity, slag and crack in the noisy and low contrast industrial radiography images. It is noticeable that the defects appear as the gradients in pixel values, therefore, the gradient based method can help to detect the defects. In this research, different methods of gradient extraction such as Soble, Perwitt and the nonlinear diffusion filter were implemented and tried to improve the edges and the defect detection. The nonlinear diffusion filter method depends on diffusion coefficient, k, and the chosen value for k can affect the exact smoothing of the edges. Thus, in this attempt, by the use of a new approach, in comparison with the conventional edge finder methods, a nonlinear diffusion filter method is repeated and the edges are smoothed in the obtained images. Then, the defects in the gradient of images are extracted by substracting the original image from the obtained image. The mentioned methods were implemented in different radiography images. The results show that the proposed method in comparison with local gradient ones such as Sobel and Perwitt methods has superiority over different radiographic images as confirmed by the radiography film interpreters.

Diffusion tensor imaging (DTI) MRI is a noninvasive imaging method of the cerebral tissues whose fibers directions are not evaluated correctly in the regions of the crossing fibers. For the same reason, the high angular resolution diffusion images (HARDI) are used for estimation of the fiber direction in each voxel. One of the main methods to specify the direction of fibers is usage of the spherical deconvolution. The spherical deconvolution is a method which is very sensitive to noise and creates negative values in the orientation distribution function (ODF) of the fiber. To solve this problem, methods such as Laplace-Beltrami regularized spherical deconvolution (LB-SD), the gradient based spherical deconvolution (GB-SD) and the constrained spherical deconvolution (CSD) are used. In this paper, the method for SD based on Wiener filter (WB-SD) is presented. Regarding the results, the direction of the crossing fibers is specified correctly. The proposed algorithm has specified the direction of the fibers as zero degree with 4.9 standard deviation and 89.9 degree with 3.6 standard deviation against two crossing fibers with 90 degree angle.

Anisotropic diffusion filtering (ADF) is widely used as an efficient method in random noise attenuation problems, and various modifications to its original version have been proposed. The main reason could be the thought that ADF preserves edge features with acceptable performance beside noise attenuation procedure. In seismic data processing, however, it should be noticed that using ADF could cause severe changes (artifacts) in the zones that are highly contaminated with random noise. In this paper, the optimum value is derived, by introducing an automatic framework based on two artificial intelligence (AI) algorithms, adaptive neuro-fuzzy inferences (ANFIS) and fuzzy c-mean clustering (FCM). The neuro-fuzzy network is trained using original data, successive ADF values are calculated for each data point, and FCM output is obtained in a weighted averaging manner adapted with estimated noise level. The trained network is, then, generalized to all data, and thus, the ANFIS optimized version of ADF, called here AOADF, is achieved. Comparison of the results of the ADF and AOADF experiments reveals that in synthetic common mid-point (CMP) gathers, the proposed method improves peak signal to noise ratio (PSNR) value, 40% higher than ADF (in the best case) and in real CMP and common offset sorted gathers, the performance of AOADF is considerably higher than ADF, in terms of random noise attenuation without adding unwanted artifacts and preserving continuity of coherence components.

The micro-resistivity imaging log is a crucial tool for measuring the heterogeneous features of a formation. It objectively and quantitatively describes various reservoir characteristics, including fine structures, thin strata, fissures, and sedimentary facies. In these imaging tools, measurements from button arrays create an electrical image of the wellbore. However, gaps between tool pads limit coverage, and damaged buttons may compromise image quality.In this study, we examine image log data for factors impacting data acquisition, followed by processing for basic correction, image enhancement, and static and dynamic image log creation. To achieve 100% coverage, the Minimum Weighted Norm Interpolation (MWNI) algorithm fills gaps between tool pads. Finally, the Anisotropic Diffusion Filter (ADF) reduces noise and enhances image log quality in MATLAB, providing a comprehensive image from logging tools. As image logs play a crucial role in illustrating the wellbore and reservoir, this study suggests a new workflow to successfully tackle the challenges linked with acquiring comprehensive image log coverage.

Background: Prostate cancer is the third leading cause of death and is the most common cause of cancer in elderly men. Regarding to the low accuracy of screening methods such as prostate-specific antigen (PSA), Digital Rectal Examination (DRE) and trans rectal ultrasound (TRUS) in detection and localization of tumor, Magnetic Resonance Imaging (MRI) and Diffusion Weighted Imaging (DWI) attracted many attentions in the past years. DWI reveals micro-molecular diffusion, which is the Brownian motion of the spins in biologic tissues. This technique can delineate pathologic lesions with high tissue contrast against generally suppressed background signal. In this paper, the value of DWI in detection of prostate cancer is studied.Methods: In this cross-sectional study, the studied population are suspicious patients to prostate cancer based on high Prostatic Specific Antigen level or abnormal Digital Rectal Examination who refered for prostate biopsy to radiology department of Hazrate- Rasoul Hospital during the year 2011. The results of DWI are compared to biopsy results for all patients.Results: Eighty five patients are selected. The DWI sensitivity in detecting of prostate cancer is 100%, specificity 97.1% and positive and negative predictive values are 89.5% and 100%, respectively. The results showed that if DWI reports the prostate cancer as negative, the result was highly reliable and if it reports as positive, although the report was not 100% reliable, but it still had high reliability, more than 90%.Conclusion: DWI had high accuracy in detecting prostate cancer for patients with cancer. Also the accuracy of this method for patients without prostate cancer was acceptable compared to the other common methods.

Background: Multiple sclerosis (MS) is a chronic disease that begins most commonly in young adults and is characterized pathologically by multiple areas of central nervous system (CNS) white matter inflammation, demyelination, and glial scarring. The most valuable aid for diagnosis is magnetic resonance imaging (MRI). New type of MRI has been developed on the basis of molecular diffusion which capable of detecting acute and active lesions. Early diagnosis and treatment is possible to stop or slowdown progression of disease. The aim of this study is to compare the findings of conventional and diffusion- weighted (DW) MRI in detection cerebral lesions of MS.Methods: Thirty patients with clinically definite MS and 30 healthy volunteers were studied in a descriptive-prospective survey over a twelve-month period of time.Conventional and DW MRI were used in both groups. Total number, morphology, location and the mean size of the intra-cerebral plaques of MS were compared between group. The sensitivity and the specificity of both imaging methods in detecting these plaques were determined.Results: Thirty patients with MS with the mean age of 32.76±8.79 years and 30 healthy individuals with the mean age of 32.75±9.23 years were enrolled. Plaque within the brain was significantly higher by the conventional method (P<0.05). Ovoid lesions were greater in number in the conventional method group. More lesions were detected by the conventional method in the areas of periventricle, centrum semiovale and corpus callosum. Regarding the size of plaque, the minimum measurement was significantly lower in the conventional method group. The sensitivity of both methods was 100%. The specificity of conventional and DW MRI was 86.6% and 96.6%, respectively.Conclusion: In our study the sensitivity of both methods was the same but the specificity of DW MRI was higher. DW MRI may detect lesions which are not detectable by the routine methods.