Domestic Construction of Soft Tissue Phantom for Validation Linear Quasi –Static Elastography
Zahra
Saghaei Noosh Abadi
M.Sc. Graduate , School of Mechanical Engineering, Iran University of Science & Technology
author
Atefe
Aghajani
Ph.D student, School of Mechanical Engineering, Iran University of Science & Technology
author
Mohammad
Haghpanahi
Associate Professor, School of Mechanical Engineering, Iran University of Science & Technology
author
text
article
2013
per
We introduce how we may produce an experimental phantom for modeling the mechanical properties of soft tissue. Gelatin materials are used to construct the phantom. Our phantom comprises of two different types of tissue; tumor and background normal tissue. Weight ratio of the dry gelatin and deionized water are obtained for producing the young’s modulus of 21 kPa and 102 kPa for the normal tissue and tumor, respectively. This phantom is used in ultrasound elastography with external excitation less than 5%.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
1
11
https://www.ijbme.org/article_13051_c0314c25bfdb3985fc5c5c0ad2992561.pdf
dx.doi.org/10.22041/ijbme.2013.13051
Empirical mode decomposition-based elimination of Electrocardiogram artifact from Electromyogram signals
Mohsen
Naji
Assistant Professor, Department of Biomedical Engineering, Dezful Branch, Islamic Azad University,
author
Seyed Mohammad
Firouzabadi
Professor, Department of Medical Physics, Tarbiat Modares University
author
Sedighe
Kahrizi
Assistant Professor, Department of Physical Therapy, Tarbiat Modares University
author
text
article
2013
per
The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from EMG signals. The method is compared to a Butterworth high pass filtering. Results obtained from the analysis of generated and experimental EMG signals show that our method outperforms the high pass filtering for elimination of ECG contamination from trunk EMG signals.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
13
20
https://www.ijbme.org/article_13052_8f36fc47ceb6b32c6972fce98f91c2ef.pdf
dx.doi.org/10.22041/ijbme.2013.13052
Classification of elderly subjects to fallers and non-fallers using a novel set of features extracted from gait frieze pattern
Ziba
Gandomkar
M.Sc., Biomedical Engineering, Motion Control and Computational Neuroscience laboratory, School of ECE, College of Engineering, University of Tehran
author
Fariba
Bahrami
Associate Professor, University of Tehran, College of Engineering, School of ECE, Motion Control and Computational Neuroscience laboratory, Control and Intelligent Processing Center of Excellence (CIPCE).
author
text
article
2013
per
Changes in gait pattern are early symptoms in many disorders such as balance and control problems resulted in fall among elderlies. This paper aims at proposing a new set of features extracted from Gait Frieze Pattern (GFP) in order to classify seniors to fallers and non-fallers. For indicating the effectiveness of the presented method, the algorithm is used for recognition of different type of abnormal gaits. The introduced method consists of three main steps: extracting the subject from background, generating GFP and aligning them, and building the proposed image from GFP by thresholding followed by morphological operations. For evaluation of the proposed features, video sequences are collected from 8 elderly fallers, 8 non-fallers, and 8 youth while performing standard Timed Up and Go (TUG) test. In addition to TUG test youths are asked to walk fast and pretend to walk with 6 different types of abnormalities (limping, waddling, anterior- posterior sway, lateral sway, dragging, steppage gait). For finding correct classification rate, each time one data is considered as test and others as train and label of train data with the most similarity with test one on the score of normalized cross correlation is assigned to test data. Comparing to conventional TUG test, correct classification data is improved around 20% for faller detection. In addition, correct classification rate for detecting of different abnormalities in gait is approximately 90%.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
21
37
https://www.ijbme.org/article_13054_5f7fab79fdf6a36af512a34d4ce38bbb.pdf
dx.doi.org/10.22041/ijbme.2013.13054
Applying Probabilistic Classification Vector Machines in Self-paced BCI to Enhance Foot Movement Detection
Rahele
Mohammadi
Ph.D Student, Biomedical Eng. Department, Electrical and Computer Eng. College, TarbiatModares University
author
Ali
Mahloojifar
Associate Professor, Biomedical Eng. Department, Electrical and Computer Eng. College, TarbiatModares University
author
text
article
2013
per
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.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
39
55
https://www.ijbme.org/article_13079_08ca1df8a038d5ac1ec5e9a13e67ce08.pdf
dx.doi.org/10.22041/ijbme.2013.13079
Interpolation of Orientation Distribution Functions (ODFs) in High Angular Resolution Diffusion Imaging
Maryam
Afzali
Ph.D student, Department of Electrical Engineering, Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology
author
Emadoddin
Fatemizadeh
Assistant Professor, Department of Electrical Engineering, Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology
author
Hamid
Soltanian Zadeh
Professor, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran.
Image Analysis Laboratory, Radiology Department, Henry Ford Health System, Detroit, Michigan, USA
author
text
article
2013
per
Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs.We find the principal direction of ODF of the dataset to be interpolated and then rotate it to lie in the direction of the reference dataset. Now that ODFs are parallel, we apply linear interpolation to generate interpolated data. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
57
64
https://www.ijbme.org/article_13080_e735af74608f2b44e6e22a2a844f8615.pdf
dx.doi.org/10.22041/ijbme.2013.13080
Four-dimensional modeling of GBM brain tumor growth using cellular automata
Seyed Hojat
Sabzpoushan
Assistant professor, Biomedical engineering department, School of electrical engineering, Iran university of Science and technology
author
Fateme
Pourhasan Zadeh
Ph.D Student, Biomedical engineering department, School of electrical engineering, Iran university of Science and technology
author
Zohre
Agin
M.Sc, Biomedical engineering department, School of electrical engineering, Iran university of Science and technology
author
text
article
2013
per
A great number of people are diagnosed with a brain tumor, annually. Glioblastoma multiform (GBM) is the most common and deadliest malignant primary brain tumor. Therefore, the study of the growth of GBM is one of the issues considered by researchers. Many mathematical models to simulate the growth of GBM brain tumor have been proposed. These models help scientists to understand the process of tumor growth in order to achieve effective treatment. To simulate the tumor growth, a four dimensional (4D) model using cellular automata (CA) method is presented in this paper. A three dimensional (3D) lattice constituted by Voronoi tessellation is used. Spatial distribution of grid points in 3D has been generated by using Random Sequential Addition (RSA). In the utilized lattice, each cell is a polyhedron with various number of edges and neighboring. Delaunay triangulation is applied to find neighboring cells. Each cell in this lattice can be necrotic, non-proliferative, proliferative, non-tumorous or normal. The simulation is capable to exhibit a tumor growth of 0.1 mm to 25 mm in radius. The proposed model has been compared with experimental data in four temporal stages: spheroid, detectable lesion, diagnosis and death. Studies show that the accuracy of the presented model is generally about 85%.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
65
73
https://www.ijbme.org/article_13081_819dbb865a8a2528927a23bbaba9643f.pdf
dx.doi.org/10.22041/ijbme.2013.13081
Stability and control of human body motion during performing balance motion in Wushu
Afsaneh
Yavari
M.Sc, Biomechanics Department, Faculty of Biomedical Engineering, Science and Research Branch of Islamic Azad Univesity
author
Mostafa
Rostami
Associate Professor, Biomechanics Department, Faculty of Biomedical Engineering, Amirkabir University of technology
author
Ali
Esteki
Professor, Medical engineering and Physics Department, Shahid Beheshti University of Medical Science
author
Ali
Tanbakoosaz
Ph.D Student, Biomechanics Department, Faculty of Biomedical Engineering, Science and Research Branch of Islamic Azad Univesity
author
Mehdi
Yousefi Azar Khanian
Ph.D Student, Biomechanics Department, Faculty of Biomedical Engineering, Amirkabir University of technology
author
text
article
2013
per
Most of the recent biomechanical researches have been focused on the stability of people with disabilities and a few researches have been done on the athletes with high balance skill.The methods of elite athletes in keeping the balance can state valuable information about balance strategies and effective parameters on balance. In this study we calculate local dynamical stability of musculoskeletal systems during a hard balance motion. Eight non elite athletes and six elite athletes in Wushu participatedin this study. Kinematic parameters for quantitative assessment of postural fluctuations were recorded by VICON ® Motion Analysis System. Using Lyapunov stability theory, stability and preparation of athletes were evaluated and the best model in performing the balance motion was shown to the coaches. Results from this study showed that motion pattern and preparation of athletes are effective in the displacements of center of mass and center of pressure and finally the stability of athletes.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
75
84
https://www.ijbme.org/article_13082_e3b124b449e27779095d98345119b411.pdf
dx.doi.org/10.22041/ijbme.2013.13082
Autoregressive Modeling of the Photoplethysmogram AC Signal Amplitude Changes after Flow-Mediated Dilation in Healthy and Diabetic Subjects
Mina
Amiri
M.Sc, Department of Electrical Engineering, Sharif University of Technology
author
Edmond
Zahedi
Associate Professor, Department of Electrical Engineering, Sharif University of Technology, Tehran.
Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, National University Malaysia
author
Fereydoun
Behnia
Assistant Professor, Department of Electrical Engineering, Sharif University of Technology
author
text
article
2013
per
It is proved that the endothelial (artery inner lumen cells) function is associated with cardiovascular risk factors. Among all the common non-invasive methods employed in the research setting for assessing endothelial function, flow-mediated dilation is the most widely used one. This technique measures endothelial function by inducing reactive hyperemia using temporary arterial occlusion and measuring the resultant relative increase in blood vessel diameter via ultrasound. In this paper, the limitations associated with the ultrasound technique are overcome by using the photoplethysmogram (PPG) signal recorded during FMD. The correctness of this approach is investigated by modeling the AC changes of PPG after FMD by a 2nd order autoregressive model. A sensitivity of 78.6%, specificity of 81.6% and total accuracy of 80% were achieved in classification of 16 healthy and 14 diabetic subjects.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
7
v.
1
no.
2013
85
95
https://www.ijbme.org/article_13083_4f7656ca02145e4f19fa6f965579b5c9.pdf
dx.doi.org/10.22041/ijbme.2013.13083