Biomedical Image Processing / Medical Image Processing
Mahdie Ghasemi; Ali Mahloojifar; Mehdi Omidi
Volume 8, Issue 3 , September 2014, , Pages 261-275
Abstract
Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. ...
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Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. In this paper, we examine changes of functional dependency between brain regions of interest associated with known anatomical pathology in Parkinson Disease (PD) using copula theory on resting state fMRI. Five types of copulas were tested: Gaussian and t (Euclidean), Clayton, Gumbel and Frank (Archimedean). We used an efficient maximum likelihood procedure for estimating copula parameters. Goodness of fits was tested using root mean square error (RMSE) and kulback-leibler divergence between each copula function and joint empirical cumulative distribution. Control vs PD group comparison was also done on dependency parameter using parametric and nonparametric tests. The results show that functional dependency between cerebellum and basal ganglia is much stronger in PD than in control. In this paper, we proposed for the first time that joint distribution characteristics could potentially provide information on discriminative features for functional connectivity analysis between healthy and patients.
Biomedical Image Processing / Medical Image Processing
Elahe Moghimirad; Ali Mahloojifar; Babak Mohammadzadeh Asl
Volume 8, Issue 3 , September 2014, , Pages 277-291
Abstract
A new implementation of a synthetic aperture focusing technique is presented in the paper. Standard medical ultrasound imaging is done using line-by-line transmission with classical Delay-and-Sum (DAS) image reconstruction. Synthetic aperture imaging, however, has a better resolution and frame rate in ...
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A new implementation of a synthetic aperture focusing technique is presented in the paper. Standard medical ultrasound imaging is done using line-by-line transmission with classical Delay-and-Sum (DAS) image reconstruction. Synthetic aperture imaging, however, has a better resolution and frame rate in cost of more computational load. To overcome this problem, block processing algorithms are used in radar and sonar which are relatively unknown in medical. To extend the methods to medical field, one should concern the parameters difference such as carrier frequency, signal band width, beam width and depth of imaging. In this paper, we extended one of these methods called wavenumber to medical ultrasound imaging with a simple model of synthetic aperture focus. We have also used chirp pulse excitation followed by matched filtering, windowing and spotlighting algorithm to compensate the effect of differences in parameters between radar and medical imaging. Computational complexity of the two reconstruction methods, wavenumber and DAS, have been calculated. Field II simulated point data has been used to evaluate the results in terms of resolution and contrast. Evaluations with simulated data show that for typical phantoms, reconstruction by wavenumber algorithm is almost 20 times faster than classical DAS while retaining the resolution.
Rehabilitation Engineering
Rahele Mohammadi; Ali Mahloojifar
Volume 7, Issue 1 , June 2013, , Pages 39-55
Abstract
A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from the continuous electroencephalogram (EEG) signal to produce a brain switch. This work shows significant improvement in a movement based self-paced BCI by applying a new sparse learning ...
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A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from the continuous electroencephalogram (EEG) signal to produce a brain switch. This work shows significant improvement in a movement based self-paced BCI by applying a new sparse learning classification algorithm, probabilistic classification vector machines (PCVMs) to classify EEG signal. Constant-Q filters instead of constant bandwidth filters for frequency decomposition are also shown to enhance the discrimination of movement related patterns from EEG patterns associated with idle state. Analysis of the data recorded from seven subjects executing foot movement using the constant-Q filters and PCVMs shows a statistically significant 16% (p<0.03) average improvement in true positive rate (TPR) and a 2% (p<0.03) reduction in false positive rate (FPR) compared with applying constant bandwidth filters and SVM classifier.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Mohammadvali’ee; Ali Mahloojifar
Volume 7, Issue 3 , June 2013, , Pages 265-276
Abstract
One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code ...
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One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code the signal into a matrix of codes, then we will use bluetooth technology to transmit data from offset device to target device. Signal coding will affect in sending and storing data. This suite of codes that form for the first time in this method, include number and type of extermumes, time of occurring them, samples of signal and etc. We complete the coding, using arithmetic coding. The input of arithmetic coding is the extracted suite of coefficients and the output is arithmetic codes. We use SD-200 serial bluetooth modules produced by SENA™ in transmission of coding coefficients. The transmitter sends extracted coefficients and receptor receives them and reconstructs the primary signal. For testing and evaluating the method, we use MIT–BIH arrhythmia database. In our method, when average Percentage of Root Mean Square Differential (PRD) is equal to 5.93%, Compression Ratio (CR) and Cross Correlation (CC) is equal to 8.69 and 99.8%, respectively. Beside, when PRD is about 10.21%, CR and CC is 13.03 and 99.47%, respectively. The maximum standard deviation of compression ratio in two states is 4.17.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Rahele Mohammadi; Ali Mahloojifar
Volume 6, Issue 2 , June 2012, , Pages 141-152
Abstract
Self-paced BCI systems are more natural for real-life applications since these systems allow the user to control the system when desired. Detection of event periods in continuous EEG signal is one of the most important challenges in designing self-paced BCIs. In this paper, the Event related synchronization ...
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Self-paced BCI systems are more natural for real-life applications since these systems allow the user to control the system when desired. Detection of event periods in continuous EEG signal is one of the most important challenges in designing self-paced BCIs. In this paper, the Event related synchronization (ERS) is extracted from idle EEG signal using fractal dimensions in frequency range from 6 to 36 Hz and sparse representation based classifier. Our proposed method applied on EEG signal recorded during executing foot movement in 7 subjects. The average true positive rate and false positive rate equal to 90% and 5% were achieved.
Biomedical Image Processing / Medical Image Processing
Babak Mohammadzadeh Asl; Ali Mahloojifar
Volume 3, Issue 1 , June 2009, , Pages 33-46
Abstract
In recent years, adaptive beam forming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive beam formers. This improvement results from the fact that their weights are chosen based on the priori knowledge ...
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In recent years, adaptive beam forming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive beam formers. This improvement results from the fact that their weights are chosen based on the priori knowledge of the received data and updated using current statistics of the array signal. Most of the adaptive beam formers presented in the ultrasound imaging literature are based on the minimum variance (MV) beam former, which can improve the imaging resolution while retaining the contrast. It is desirable that the beam former could improve the resolution and contrast, at the same time. To this end, in this paper, we have used temporal averaging besides the conventional spatial averaging to estimate the more accurate covariance matrix. Moreover, we have used the coherence factor weighting combined with MV beam forming to enhance the focusing quality and hence reducing the undesired side lobes. The efficacy of the proposed adaptive beam forming approach is demonstrated via a number of simulated and experimental examples.
Seyed Mahmoud Sakhaei; Ali Mahloojifar
Volume -2, Issue 1 , July 2005, , Pages 47-56
Abstract
The beam pattern profile of an ultrasound array is of great importance in ultrasound imaging. This profile could be enhanced by weighting the elements of array. However, this technique will decreases the signal to noise ratio (S/N) and consequently the quality of the obtained image. In this study, the ...
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The beam pattern profile of an ultrasound array is of great importance in ultrasound imaging. This profile could be enhanced by weighting the elements of array. However, this technique will decreases the signal to noise ratio (S/N) and consequently the quality of the obtained image. In this study, the S/N variation in weighting process is mathematically analyzed, and a new method is proposed to optimize the weighting parameters. The main objective of the method is to provide the desired output of the beam pattern profile of the ultrasound array, as well as the highest possible S/N. The results show that S/N decreases with increasing the main lobe width of beam pattern. The decrease of S/N by weighting in full arrays is higher than in the sparse ones. Also, reducing the focusing depth has the same effect on S/N.
