Biomedical Imaging / Medical Imaging
Neda Sardaripour; Alireza Sedghi; Ali Yoonessi; Ali Khadem; Hamid Abrishami Moghaddam
Volume 12, Issue 4 , January 2019, , Pages 299-315
Abstract
During vision process, the information produced by rod and cone photoreceptors is compressed in retina and then is transmitted by three separated pathways of ganglion cells, Magno, Parvo and Konio, to the upper level processing centers. There are electrophysiological and psychophysical evidences that ...
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During vision process, the information produced by rod and cone photoreceptors is compressed in retina and then is transmitted by three separated pathways of ganglion cells, Magno, Parvo and Konio, to the upper level processing centers. There are electrophysiological and psychophysical evidences that these three pathways show characteristic patterns of malfunction in multiple sclerosis (MS) patients. Although fMRI can provide accurate localization of the neural activities in these pathways, there is no fMRI study on malfunctions of these pathwyas in MS yet. So by employing the differences in structure and function of these cells, we generated three different visual stimuli with different spatial and temporal frequencies to stimulate each pathway separately. These stimuli were shown to the subject inside MRI scanner by a calibrated projector located outside of scanner room. The fMRI data were acquired from two groups of normal and MS subjects (each including 5 subjects) by using a standard protocol. Finally, the activation results in visual lobe and LGN were analyzed in within-group and between-group levels. The group analysis of fMRI data was performed by using general linear modeling (GLM) and fixed-effect method via FSL software and results showed patterns of malfunctions in visual cortex and LGN in MS group. Also, among Magno, Parvo, and Konio cellular pathways in LGN, just the activation of Magno cellular pathway showed significant malfunction in MS group.
Bioelectromagnetics
Mohammad Reza Yousefi; Reza Jafari; Hamid Abrishami Moghaddam
Volume 8, Issue 1 , March 2014, , Pages 69-86
Abstract
In this paper, a combined wavelet based mesh free method has been presented to solve the forward problem in magnetic induction tomography (MIT). Being a non-contact safe imaging technique, MIT has been an appropriate method for noninvasive industrial and medical imaging. In this imaging method, a primary ...
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In this paper, a combined wavelet based mesh free method has been presented to solve the forward problem in magnetic induction tomography (MIT). Being a non-contact safe imaging technique, MIT has been an appropriate method for noninvasive industrial and medical imaging. In this imaging method, a primary magnetic field is applied by one or more excitation coils to induce eddy currents in the material to be studied, and then the secondary magnetic field from these eddy currents is detected in sensing coils. Image reconstruction is obtained from estimated electric conductivity coefficients by using measurement data and solutions of forward and inverse problems. In general, the forward problem is solved using finite element method (FEM) with acceptable accuracy but in problems involving moving objects or objects with changing geometrical appearance, mesh distortion is inevitable and susceptible to producing error in numerical results. Since the solution of the FEM depends on the mesh shape and boundary condition constraints are difficult to be applied to the mesh free method, in this paper, the combined wavelet based mesh free approach is suggested to resolve the disadvantages of both methods in the MIT forward problem. In order to apply interface conditions between the two finite element and mesh free sub-domains, slope jump functions are entered to the set of basis functions. The simulation results obtained by the proposed method are compared with the FEM in terms of accuracy and computational cost.
Biomedical Image Processing / Medical Image Processing
Maryam Momeni; Hamid Abrishami Moghaddam; Reinhard Grebe; Kamran Kazemi; Fabrice Wallois
Volume 5, Issue 3 , June 2011, , Pages 231-244
Abstract
Reliable gradation of neonatal brain development is important for clinical investigation of neurological disorders. A prerequisite for such quantification of development is knowledge about an appropriate temporal resolvability. For this purpose, we investigated the evolution of macroscopic morphological ...
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Reliable gradation of neonatal brain development is important for clinical investigation of neurological disorders. A prerequisite for such quantification of development is knowledge about an appropriate temporal resolvability. For this purpose, we investigated the evolution of macroscopic morphological features of the neonatal brain to estimate, for the first time, the required temporal interval in the early weeks after birth. In a first step, we constructed two neonatal templates for the age ranges of 39-40 and 41- 42 weeks' gestational age using T1-weighted MR images. We compared the spatial variation of anatomical landmarks and the average and the maximal length of spatial deformation in 25 subjects normalized to the two templates along x, y and z directions. MANOVA confirmed the significant difference between spatial variations of the above macroscopic features in the two age ranges. Furthermore, quantitative analysis of feature scattering yielded the same result even in features for which the null hypothesis was not rejected by MANOVA. We conclude that minimal temporal interval of two weeks is required for acute macroscopic morphological studies of the developing brain in the early weeks after birth.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Maryam Momeni; Kamran Kazemi; Reinhard Grebe; Fabrice Wallois
Volume 4, Issue 4 , June 2010, , Pages 337-360
Abstract
Diagnostic follow-up of the brain development during the neonatal period and childhood is an important clinical task. Any disturbance of this process can cause pathological deviations, especially if the baby is born premature. Recent advances in magnetic resonance imaging allow obtaining high-resolution ...
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Diagnostic follow-up of the brain development during the neonatal period and childhood is an important clinical task. Any disturbance of this process can cause pathological deviations, especially if the baby is born premature. Recent advances in magnetic resonance imaging allow obtaining high-resolution images of the neonatal brain. After segmenting the brains they can be used to reconstruct and model changes occurring during neonatal brain development. In addition such near-realistic model of the head, including the skin, skull and brain can be used to solve the inverse problem of determining the sources of registered signals from electrical brain activity. Although there exist numerous methods and various modeling schemes for adults, these cannot be used directly for neonates due to important differences in morphology. In this review article, neonatal brain atlases are divided into three categories: individual atlases, probabilistic atlases and stochastic atlases. In the following, existing neonatal brain atlases are placed in this classification and their methods of construction are presented. Furthermore, strengths and weaknesses of those neonatal brain atlases are analyzed and finally future research trends in this area are explained.
Biomedical Image Processing / Medical Image Processing
Azar Tolouee; Hamid Abrishami Moghaddam; Masoume Giti
Volume 2, Issue 3 , June 2008, , Pages 179-189
Abstract
Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted ...
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Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted using a new machine learning approach. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames are utilized to extract the features from the defined regions of interest (ROJs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. Our experiments in lung pattern classification were rendered on four different lung tissue patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images from 17 subjects. After applying the technique to classify these patterns in small ROis, we extended the classification scheme to the whole lung in order to produce the quantitative scores of abnormalities in lung parenchyma of the patients. The performance of the proposed method was compared with two state-of-the-art computer based methods for lung tissue characterization. It was also validated against the experienced observers. The average kappa statistic of agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the interobserver agreement was 0.6848. This computer system can approach the performance of the expert observers in the diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.
Biomedical Image Processing / Medical Image Processing
Saeed Kermani; Hamid Abrishami Moghaddam; Mohammad Hasan Moradi
Volume 2, Issue 3 , June 2008, , Pages 215-231
Abstract
This paper presents a new method for quantification analysis of left ventricular performance from the sequences of cardiac magnetic resonance imaging using the three-dimension active mesh model (3DAMM). AMM is composed of topology and geometry of L V and associated elastic material properties. The ...
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This paper presents a new method for quantification analysis of left ventricular performance from the sequences of cardiac magnetic resonance imaging using the three-dimension active mesh model (3DAMM). AMM is composed of topology and geometry of L V and associated elastic material properties. The LV deformation is estimated by fitting the model to the initial sparse displacements which is measured by a new establishing point correspondence procedure. To improve the model, a new shape-based interpolation algorithm was proposed for reconstruction of the intermediate slices. The proposed approach is capable of estimating the displacement field for every desired point of the myocardial wall. Then it leads to measure dense motion field and the local dynamic parameters such as Lagrangian strain. To evaluate the performance of the proposed algorithm, eight image sequences (six real and two synthetic sets) were used and the findings were compared with those reported by other researchers. For synthetic image sequence sets, the mean square error between the length of motion field estimated by the Algorithm and the analytical values was less than 0.5 mm. The results showed that the strain measurements of the normal cases were generally consistent with the previously published values. The results of analysis on a patient data set were also consistent with his clinical evidence. In conclusion, the results demonstrated the superiority of the novel strategy with respect to our formerly presented algorithm. Furthermore, the results are comparable to the current state-of-the-art methods.
Biomedical Image Processing / Medical Image Processing
Hadi Jafariani; Hamid Abrishami Moghaddam; Mohammad Shahram Moein
Volume 1, Issue 4 , June 2007, , Pages 311-318
Abstract
One of the most accurate techniques for human identification is based on the uniqueness of the retinal blood vessels pattern. In this paper, we present a new approach for human identification using retina image. This approach is insensitive to rotation, scaling and translation. The Fourier-Mellin transform ...
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One of the most accurate techniques for human identification is based on the uniqueness of the retinal blood vessels pattern. In this paper, we present a new approach for human identification using retina image. This approach is insensitive to rotation, scaling and translation. The Fourier-Mellin transform coefficients and moments of the retinal image were used to extract the suitable features. To compensate the rotational effects caused by different relative positions of the retina scanner with respect to the eye, a rotation compensator was designed. For retinal image interpretation, the optic disc location was considered as a fixed and reference point. For its localization, the Haar wavelet and the Snakes model were used. The experimental results demonstrated an error rate close to zero for the proposed method.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Alireza Sheikh Hasani; Abbas Mostafa; Masoume Giti; Parviz Abdolmaleki
Volume -1, Issue 2 , June 2005, , Pages 117-128
Abstract
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet ...
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This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet transform of the image is computed. Then two wavelet coefficients as well as two statistical features are used with a neural network for a primary classification of the image pixels. In the second stage, some noisy pixels extracted by the first step are eliminated. Then 18 features defined for each microcalcification are used with a nonlinear classifier for accurate detection of microcalcifications. For training of this classifier we used 16 regions from a database containing 379 microcalcifications. Finally, in the third stage five features defined for each microcalcification cluster with a neural network are used to recognize malignant microcalcification clusters. For training of this network, 22 clusters including 8 malignant and 14 benign cases were used. The performance of the algorithm was evaluated using a separate image set composed of 22 clusters including 10 malignant and 12 benign cases. Using these tests images and the threshold value of 0.45, the sensitivity of the algorithm was 100% and its specificity was 91.6%.
