Showing papers in "International Journal of Imaging Systems and Technology in 2015"
TL;DR: A novel automatic classification system based on particle swarm optimization and artificial bee colony and three new variants of feed‐forward neural network (FNN), consisting of IABAP‐fNN, ABC‐SPSO‐FNN, and HPA‐Fnn, which was superior to existing state‐of‐the‐art methods in terms of classification accuracy.
Abstract: Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system based on particle swarm optimization PSO and artificial bee colony ABC, with the aim of distinguishing abnormal brains from normal brains in MRI scanning. The proposed method used stationary wavelet transform SWT to extract features from MR brain images. SWT is translation-invariant and performed well even the image suffered from slight translation. Next, principal component analysis PCA was harnessed to reduce the SWT coefficients. Based on three different hybridization methods of PSO and ABC, we proposed three new variants of feed-forward neural network FNN, consisting of IABAP-FNN, ABC-SPSO-FNN, and HPA-FNN. The 10 runs of K-fold cross validation result showed the proposed HPA-FNN was superior to not only other two proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the method achieved perfect classification on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT+PCA+HPA-FNN method excelled existing methods. It can be applied to practical use.
TL;DR: WFRFT is effective, the proposed methods can be used in practical, and all three proposed methods were superior to eight state‐of‐the‐art algorithms.
Abstract: To classify brain images into pathological or healthy is a key pre-clinical state for patients. Manual classification is tiresome, expensive, time-consuming, and irreproducible. In this study, we aimed to present an automatic computer-aided system for brain-image classification. We used 90 T2-weighted images obtained by magnetic resonance images. First, we used weighted-type fractional Fourier transform WFRFT to extract spectrums from each magnetic resonance image. Second, we used principal component analysis PCA to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine SVM and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5-fold cross-validation results showed that this proposed "WFRFT+PCA+generalized eigenvalue proximal SVM" yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed "WFRFT+PCA+twin SVM" and better than the proposed "WFRFT+PCA+SVM." Besides, all three proposed methods were superior to eight state-of-the-art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 317-327, 2015
TL;DR: The proposed approach classifies the mammographic image, MRI images, CT images, and ultrasound images as either normal or abnormal, and it is observed that the classification rate is almost high under the RBF neural network for all the dataset considered.
Abstract: In this article, we analyze the performance of artificial neural network, in classification of medical images using wavelets as feature extractor. This work classifies the mammographic image, MRI images, CT images, and ultrasound images as either normal or abnormal. We have tested the proposed approach using 50 mammogram images 13 normal and 37 abnormal, 24 MRI brain images 9 normal and 15 abnormal, 33 CT images 11 normal and 22 abnormal, and 20 ultrasound images 6 normal and 14 abnormal. Four kind of neural network models such as BPN Back Propagation Network, Hopfield, RBF Radial Basis Function, and PNN Probabilistic neural network were chosen for study. To improve diagnostic accuracy, the feature extracted using wavelets such as Harr, Daubechies db2, db4, and db8, Biorthogonal and Coiflet wavelets are given as input to the neural network models. Good classification percentage of 96% was achieved using the RBF when Daubechies db4 wavelet based feature extraction was used. We observed that the classification rate is almost high under the RBF neural network for all the dataset considered. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 33-40, 2015
TL;DR: This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C‐Means clustering algorithm, that helps segment the target and automatically calculates the lesion volume.
Abstract: Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index = 95.59%, Jaccard Index = 91.86%, Sensitivity = 97.39%, Specificity = 94.30%, Mean Absolute Distance = 0.246[pixels], Maximum Distance = 1.050[pixels], and Hausdorff Distance = 1.365[pixels]. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 213–225, 2015
TL;DR: This article describes how the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification, and the performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value(NPV).
Abstract: Magnetic Resonance Imaging MRI is an advanced medical imaging technique that has proven to be an effective tool in the study of the human brain. In this article, the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification. Histogram equalization is used in enhancement stage, gray level co-occurrence matrix and wavelets are used as features and these extracted features are trained and classified using Support Vector Machine SVM classifier. The tumor region is detected using morphological operations. The performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value PPV, and negative predictive value NPV. The proposed system achieved 0.95% of sensitivity rate, 0.96% of specificity rate, 0.94% of accuracy rate, 0.78% of PPV, and 0.87% of NPV, respectively. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 297-301, 2015
TL;DR: The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR.
Abstract: In the process of medical image formation, the medical image is often interfered by various factors, and it is deteriorated by some new noise that may reduce the quality of the obtained image, which affect the clinical diagnosis seriously. A new medical image enhancement method is proposed in this article. Firstly, the initial medical image is decomposed into the NSCT domain with a low-frequency sub-band, and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. An adaptive thresholding method is used for denoising the coefficients of the high-frequency sub-bands. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 199–205, 2015
TL;DR: A computer‐aided diagnosis (CAD) method using T1‐weighted magnetic resonance imaging (MRI) to differentiate PD from controls is developed.
Abstract: Introduction Parkinson's disease (PD) is a neurological disorder, which is diagnosed on the basis of clinical history and examination alone as there are no diagnostic tests available. However, the current diagnosis highly depends on the knowledge and experience of clinicians and hence subjective in nature. Thus, the focus of this study is to develop a computer-aided diagnosis (CAD) method using T1-weighted magnetic resonance imaging (MRI) to differentiate PD from controls. Method: The proposed method utilizes graph-theory-based spectral feature selection method to select a set of discriminating features from whole brain volume. A decision model is built using support vector machine as a classifier with leave-one-out cross-validation scheme. The performance measures, namely, sensitivity, specificity, and classification accuracy, are utilized to evaluate the performance of the decision model. The efficacy of the proposed method is checked on volumetric 3D T1-weighted (1 mm iso-voxel) MRI dataset of 30 PD patients and 30 age and gender matched controls acquired with 1.5T MRI scanner. Results: Experimental results demonstrate that the proposed method is able to differentiate PD from controls with an accuracy of 86.67%, which encourages the use of CAD. The performance of the proposed method outperforms the existing methods except one. In addition, it is observed that the maximum number of selected features belong to caudate region followed by cuneus region. Thus, these regions may be considered as potential biomarkers in diagnosis of PD. Conclusion The proposed method may be utilized by the clinicians for diagnosis of PD. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 245–255, 2015
TL;DR: The sinogram model is suitable for future CT system; the in‐plane model is restricted to some special clinical diagnoses; and the z‐axis model is practicable for current general clinical CT images.
Abstract: The general framework of super resolution in computed tomography CT system is introduced. Two data acquisition ways before or after the reconstruction respectively are described. Three models including the sinogram model, the in-plane model and the z-axis model, are addressed to adapt super resolution to CT system. The improved iterative back projection algorithm is used in this work. Experimental results based on simulated data, GE performance phantom scanned by GE LightSpeed VCT system, one patient volunteer scanned by TOSHIBA Aquilion system, and a special experimental apparatus demonstrate that super resolution is effective to improve the resolution of CT images. The sinogram model is suitable for future CT system; the in-plane model is restricted to some special clinical diagnoses; and the z-axis model is practicable for current general clinical CT images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 92-101, 2015
TL;DR: Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images.
Abstract: In order to solve the problem of noise amplification, low contrast and image distortion in the process of medical image enhancement, a new algorithm is proposed which combines NSCT nonsubsampled contourlet transform and improved fuzzy contrast The image is decomposed by NSCT Firstly, linear enhancement method is used in low frequency coefficients; secondly the improved adaptive threshold function is used to deal with the high frequency coefficients Finally, the improved fuzzy contrast is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images © 2015 Wiley Periodicals, Inc Int J Imaging Syst Technol, 25, 7-14, 2015
TL;DR: A simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI) that is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐mean, fuzzy c‐means, etc.
Abstract: In this work, a simple and efficient CAD computer-aided diagnostic system is proposed for tumor detection from brain magnetic resonance imaging MRI. Poor contrast MR images are preprocessed by using morphological operations and DSR dynamic stochastic resonance technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest ROI lies in the middle and its size ranges from 240 × 240 mm2 to 280 × 280 mm2. The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block-based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well-known existing techniques, like k-means, fuzzy c-means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.
TL;DR: Experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art medical image fusion methods and can both preserve the information of the source images well and suppress pixel distortion.
Abstract: Sum-modified-Laplacian (SML) plays an important role in medical image fusion. However, fused rules based on larger SML always lead to fusion image distortion in transform domain image fusion or image information loss in spatial domain image fusion. Combined with average filter and median filter, a new medical image fusion method based on improved SML (ISML) is proposed. First, a basic fused image is gained by ISML, which is used for evaluation of the selection map of medical images. Second, difference images can be obtained by subtracting average image of all sources of medical images. Finally, basic fused image can be refined by difference images. The algorithm can both preserve the information of the source images well and suppress pixel distortion. Experimental results demonstrate that the proposed method outperforms the state-of-the-art medical image fusion methods. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 206–212, 2015
TL;DR: Experimental results show that the proposed medical image fusion method is superior to other methods in both subjectively visual performance and objective criteria.
Abstract: Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most approaches have not touched the low rank nature of matrix formed by medical image, which usually lead to fusion image distortion and image information loss. These methods also often lack universality when dealing with different kinds of medical images. In this article, we propose a novel medical image fusion to overcome aforementioned issues on existing methods with the aid of low rank matrix approximation with nuclear norm minimization NNM constraint. The workflow of our method is described as: firstly, nonlocal similar patches across the medical image are searched by block matching for local patch in source images. Second, a fused matrix is stacking by shared nonlocal similarity patches, then the low rank matrix approximation methods under nuclear norm minimization can be used to recover low rank feature of fused matrix. Finally, fused image can be gotten by aggregating all the fused patches. Experimental results show that the proposed method is superior to other methods in both subjectively visual performance and objective criteria. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 310-316, 2015
TL;DR: The quality of the enhanced brain image obtained after the modified histogram equalization using real coded genetic algorithm can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc.
Abstract: Histogram equalization is a well-known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm MHERCGA is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 24-32, 2015
TL;DR: The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls and outperformed the existing methods.
Abstract: Early and antemortem diagnosis of Alzheimer's disease AD may help in the development of appropriate treatment and in slowing down the disease progression. In this work, a three-phase computer aided approach is suggested for classification of AD patients and controls using T1-weighted MRI. In the first phase, smoothed modulated gray matter GM probability maps are obtained from T1-weighted MRIs. In the second phase, 3D discrete wavelet transform is applied on GM of five brain regions, which are well-documented regions affected in AD, to construct features. In the third phase, a minimal set of relevant and nonredundant features are obtained using Fisher's discriminant ratio and minimum redundancy maximum relevance feature selection methods. To check the efficacy of the proposed approach, experiments were carried out on three datasets derived from the publicly available OASIS database, using three commonly used classifiers. The performance of the proposed approach was evaluated using three performance measures namely sensitivity, specificity and classification accuracy. Further, the proposed approach was compared with the existing state-of-the-art techniques in terms of three performance measures, ROC curves, scoring and computation time. Irrespective of the datasets and the classifiers, the proposed method outperformed the existing methods. In addition, the statistical test also demonstrated that the proposed method is significantly better in comparison to the other existing methods. The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls.
TL;DR: This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2-weighted MRI brain images and compared to conventional FCM and k-means clustering techniques.
Abstract: This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2-weighted MRI brain images using the principle of modified minimum error thresholding MET method. Initially, modified MET method is applied to produce well segmented and sub-structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy PA, dice coefficient DC, and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k-means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2-weighted MRI brain images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 77-85, 2015
TL;DR: This work integrated DCT into NLML to produce an improved MRI filtration process and shows that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace.
Abstract: The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state-of-the-art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015
TL;DR: Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.
Abstract: Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio CR, low mean square error MSE, bits per pixel BPP, high peak signal to noise ratio PSNR, input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation NNBP and neural network radial basis function NNRBF applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.
TL;DR: This work proposed a multiview consensus clustering methodology for the integration of multimodal MR images into a unified segmentation aiming at heterogeneity assessment in tumoral lesions.
Abstract: It has been shown that the combination of multimodal magnetic resonance imaging MRI images can improve the discrimination of diseased tissue The fusion of dissimilar imaging data for classification and segmentation purposes, however, is not a trivial task, as there is an inherent difference in information domains, dimensionality, and scales This work proposed a multiview consensus clustering methodology for the integration of multimodal MR images into a unified segmentation aiming at heterogeneity assessment in tumoral lesions Using a variety of metrics and distance functions this multiview imaging approach calculated multiple vectorial dissimilarity-spaces for each MRI modality and it maked use of cluster ensembles to combine a set of unsupervised base segmentations into an unified partition of the voxel-based data The methodology was demonstrated with simulated data in application to dynamic contrast enhanced MRI and diffusion tensor imaging MR, for which a manifold learning step was implemented in order to account for the geometric constrains of the high dimensional diffusion information © 2015 Wiley Periodicals, Inc Int J Imaging Syst Technol, 25, 56-67, 2015
TL;DR: The presented phase MRI method demonstrated to be reliable, and the improved phase measure has achieved a sensitivity level of 0.2° for detecting any significant phase signal changes under a practical length of fMRI session.
Abstract: Purpose: Brain activity-associated neuronal currents produce weak transient magnetic fields that would affect both magnitude and phase of the local MRI signal, but these very small signal changes are not reliably detectable with conventional fMRI methodologies. A recent simulation study, using a realistic model specifically for human cerebral cortex, indicates that the phase signal change induced by spontaneous activity may reach a detectable level up to 0.2i¾? in favorable conditions. This study aimed to investigate neuronal current-induced signal changes in human visual cortex with phase MRI. Materials and Methods: Six healthy subjects participated in a phase fMRI study using a temporally well-controlled visual stimulation paradigm with a known neuronal firing pattern in visual cortex. The precise timing of the paradigm provides a means of detecting and testing the neuronal current-induced phase signal changes, and placing a series of acquisition windows to fully cover the entire response duration enables a thorough detection of any detectable phase signal changes induced by the stimulus-evoked neuronal currents. Results: The presented phase MRI method demonstrated to be reliable, and the improved phase measure has achieved a sensitivity level of 0.2i¾? for detecting any significant phase signal changes under a practical length of fMRI session. The test found no sign of any significant neuronal current-induced phase signal changes in any subject and study. Conclusions: Under the experimental condition, the upper limit of the neuronal current-induced phase signal changes was found to be less than 0.2i¾? in the human visual cortex, consistent with the model prediction.
TL;DR: This article uses the Xilinx System Generator to be implemented on a Field Programmable Gate Array (FPGA) and proposes a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization (HAMPSO) algorithm for MRI images segmentation.
Abstract: Magnetic resonance imaging MRI is considered as a key part in therapeutic procedures because it clearly defines the aim. It also avoids sensitive organs and it determines the desired paths. This phenomenon requires image processing operations such as segmentation to locate the tumor. Medical image segmentation is still an important topic in the field of brain tumor. In the present article, we propose a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization HAMPSO algorithm for MRI images segmentation. To achieve this, we use the Xilinx System Generator XSG to be implemented on a Field Programmable Gate Array FPGA. This architecture is based on a new variant of objective function. These performances of the proposed method are proved using a set of MRI images and were compared to the Hardware Architecture of segmentation based on Particle Swarm Optimization HAPSO in terms of either device utilization, execution time, qualitatively or quantitatively results.
TL;DR: The proposed approach to estimating intravoxel fiber architecture in low angular resolution dMRI is able to better resolve fiber architectures while correctly preserving image edge information, which provides a new tool for investigating the microstructures of biological tissues and for fiber tractography.
Abstract: In diffusion magnetic resonance imaging dMRI, the accuracy of fiber tracking and analysis depends directly on that of intravoxel fiber architecture reconstruction. Several methods have been proposed that estimate intravoxel fiber architecture using low angular resolution acquisitions owing to their shorter acquisition time and relatively low b-values. But these methods are highly sensitive to noise. We propose an approach to estimating intravoxel fiber architecture in low angular resolution dMRI. The method consists in using a constrained compressed sensing CCS method, also known as crossing fiber angular resolution of intravoxel architecture CFARI technique, in combination with multitensor adaptive smoothing in which a diffusion-weighted DW image smoothing scheme is constructed according to the properties of the multitensor field estimated using CFARI. The results on synthetic, physical phantom and real brain DW images show that the proposed method is able to better resolve fiber architectures while correctly preserving image edge information, which provides a new tool for investigating the microstructures of biological tissues and for fiber tractography. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 285-296, 2015
TL;DR: An adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package, and the ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm.
Abstract: In this article, for the reconstruction of the positron emission tomography PET images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm ANFIS-EM. This expectation maximization EM algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS-EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS-EM minimizes the EM objective function using maximum a posteriori MAP method. In proposed method, the ANFIS-EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de-noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS-EM algorithm provides 40% better peak signal to noise ratio PSNR when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1-6, 2015
TL;DR: Experimental results show that the proposed FCAMF produces excellent results and outperforms existing state‐of‐the‐art filters, especially for highly noisy image.
Abstract: Composite filters based on morphological operators are getting considerably attractive to medical image denoising. Most of such composite filters depend on classical morphological operators. In this article, an optimal composite adaptive morphological filter FCAMF is developed through a genetic programming GP training algorithm by using new nonlocal amoeba morphological operators. On one hand, we propose a novel method for formulating and implementing nonlocal amoeba structuring elements SEs for input-adaptive morphological operators. The nonlocal amoeba SEs in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. On the other hand, GP as a supervised learning algorithm is employed for building the FCAMF. In GP module, FCAMF is evolved through evaluating the fitness of several individuals over certain number of generations. The proposed method does not need any prior information about the Rician noise variance. Experimental results on both standard simulated and real MRI data sets show that the proposed filter produces excellent results and outperforms existing state-of-the-art filters, especially for highly noisy image. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 15-23, 2015
TL;DR: An accurate disparity vector prediction (DVP) algorithm for multi‐view video coding is proposed that uses the geometry of the camera position to calculate the parallax of different viewpoints in this algorithm and thisParallax is the foundation of DVP.
Abstract: This article proposed an accurate disparity vector prediction DVP algorithm for multi-view video coding. Differing from traditional DVP that uses the information of motion vectors of neighboring blocks, the geometry of the camera position is utilized to calculate the parallax of different viewpoints in this algorithm and this parallax is the foundation of DVP. We jointly applied the Just-Noticeable-Difference human visual model to the DVP. After filtered using Gaussian function, the geometric DVP was obtained. Experimental results showed that the proposed method achieved significant data reduction and subjective/objective quality enhancement. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 41-49, 2015
TL;DR: The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K‐mean algorithm called WFF‐K‐means and modified cuckoo search (MCS) and SVM (Multi‐Class Support Vector Machine) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant.
Abstract: The present article proposes a novel computer-aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi-Class Support Vector Machine (MC-SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K-means algorithm called WFF-K-means and modified cuckoo search (MCS) and K-means algorithm called MCS-K-means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K-means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO-K-means, color converted K-means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC-SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226–244, 2015
TL;DR: This article addresses the problem of reconstructing a magnetic resonance image from highly undersampled data, which frequently arises in accelerated magnetic resonance imaging, by proposing to impose sparsity of first and second order difference sparse coefficients within the complement of the known support.
Abstract: This article addresses the problem of reconstructing a magnetic resonance image from highly undersampled data, which frequently arises in accelerated magnetic resonance imaging. We propose to impose sparsity of first and second order difference sparse coefficients within the complement of the known support. Second order variation is involved to overcome blocky effects and support information is used to reduce the sampling rate further. The resulting optimization problem consists of a data fidelity term and first-second order variation terms penalizing entries within the complement of the known support. The efficient split Bregman algorithm is used to solve the problem. Reconstruction results from magnetic resonance imaging data corresponding to different sampling rates are shown to illustrate the performance of the proposed method. Then, we also assess the tolerance of the new method to noise briefly. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 277-284, 2015
TL;DR: Evaluating MRI issues for a power injection system at 7‐Tesla including magnetic field interaction and 31 injector performance tests found no discernible image artifacts or degradation when the power controls and powerhead were positioned in the worst‐case position.
Abstract: The injection of contrast media enables the visualization of various pathologies through X-ray, computed tomography, magnetic resonance imaging MRI, or other medical imaging modalities. Safety and functionality of power injector systems must be evaluated at high magnetic fields 7-Tesla. The purpose of this study was to evaluate MRI issues for a power injection system OptiStar Elite MR Contrast Delivery System, Mallinckrodt Pharmaceuticals, Hazelwood, MO at 7-Tesla including magnetic field interaction and 31 injector performance tests. Additionally, effects of operation of the power injection system on MR images were evaluated through observation of image artifacts and signal to noise analysis. The components of the power injection system powerhead, syringes exhibited no translational attraction when close to 7-Tesla MR system. Translational attraction was detected for the power supply, power control, and cables, therefore there is a requirement to maintain these components fixed outside of the 0.1000 Tesla line. 7-Tesla MR scanning did not impact power injection performance. The power injection system produced no discernible image artifacts or degradation when the power controls and powerhead were positioned in the worst-case position. The power injection system tested for MRI issues passed evaluation for safety and operation at 7-Tesla and is labeled MR Conditional for use at 7-T. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 50-55, 2015
TL;DR: The results suggest that KOK may improve functional outcome by inhibiting inflammatory cytokines (TNF‐α, IL‐1β, and IL-1α) in neuronal injury such as ischemic stroke.
Abstract: Kyung-ok-ko KOK has been used for the treatment of central nervous system disorders such as amnesia, dementia, and cerebral ischemia. However, the effects of KOK on transient ischemic-induced neuronal damage are still unclear. We examined whether KOK improves functional recovery and has a neuroprotective effect on infarction volume after transient middle cerebral artery occlusion MCAO. KOK 50, 100, and 200 mg/kg was administered orally following reperfusion and twice per day for 14 days post-MCAO. Infarction volume was measured using 2% 2-3-5 triphenylterazolium TTC staining at 14 days post-MCAO and alteration in regional cerebral blood flow rCBF after KOK treatment was monitored. Functional improvement was evaluated using adhesive removal and treadmill tests at 1, 7, and 14 days post-MCAO. Also, apoptotic cell death was assessed by terminal deoxynucleotidyl-transferase mediated d-UTP-biotin nick end TUNEL in the peri-infarction region. The protein level of inflammatory cytokines such as tumor necrosis factor-α TNF-α, interleukin-1α IL-1α, and interleukin-1β IL-1β was measured in the ischemic core, ischemic border zone, and contralateral hemisphere regions. The KOK-treated group showed both reduced infarction volume and behavior tests demonstrated a significant improvement as compared to the control. Also, in the KOK-treated group, rCBF was recovered to near normal levels. The apoptotic cells were significantly decreased as compared with the control group in the ischemic peri-infarction area. Furthermore, the level of TNF-α, IL-1β, and IL-1α was decreased. These results suggest that KOK may improve functional outcome by inhibiting inflammatory cytokines TNF-α, IL-1β, and IL-1α in neuronal injury such as ischemic stroke.
TL;DR: The B1 insensitivities of different nonadiabatic RF pulse techniques were calculated by using the Bloch equation, and their effectiveness was evaluated by simulation using the measured B1 field map, finding the RF pulse train was the most effective.
Abstract: Nonadiabatic frequency-selective fat-suppression radiofrequency RF pulses are simpler than adiabatic RF pulses because nonadiabatic RF pulses are only amplitude modulated. The specific absorption rate SAR is lower. However, nonadiabatic RF pulses tend to be sensitive to B1 inhomogeneity. The purpose of this research was to evaluate whether conventional adiabatic RF pulses can replace nonadiabatic RF pulse techniques. The B1 insensitivities of nonadiabatic frequency-selective fat-suppression RF pulse techniques were calculated by using the Bloch equation, and their effectiveness was evaluated by simulation using the measured B1 field map. The B1 insensitivities were compared quantitatively. The B1 insensitivities of the nonadiabatic RF pulse techniques were ±5% CHESS, a maximum of ±20% inversion recovery RF pulse with the best inversion time, ±25% RF pulse train with two subpulses, and ±44% RF pulse train with three subpulses. The RF pulse train was the most effective. The B1 insensitivity of different nonadiabatic RF pulse techniques was specified quantitatively. These results can be used to judge which nonadiabatic RF pulses can replace adiabatic RF pulses. Nonadiabatic RF pulses can reduce the SAR without compromising the image quality and would be useful in higher field-strength MRI. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 86-91, 2015
TL;DR: A novel filter is proposed in this article unveiling a simple, efficient, and fast deblurring process that involves few parameters, low calculations and does not utilize the undesirable iterative property or introduce the commondeblurring problems.
Abstract: Deblurring computed tomography (CT) images has been an active research topic in recent years because of the wide variety of challenges it offers. Hence, a novel filter is proposed in this article unveiling a simple, efficient, and fast deblurring process that involves few parameters, low calculations and does not utilize the undesirable iterative property or introduce the common deblurring problems. The newly proposed filter is validated on both real and synthetic blurred CT images to provide a sufficient understanding about its performance. Moreover, proper comparisons are made with high-profile deblurring methods, in which the results are evaluated using three reliable quality metrics of feature similarity index (FSIM), structural similarity (SSIM), and visual information fidelity in pixel domain (VIFP). The intensive experiments and performance evaluations exhibited the efficiency of the proposed filter, in that it outperformed all the comparative methods.