Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Saba Jaafari Kia; Hamid Behnam; Majid Vafaeezadeh; Ali Hosseinsabet
Volume 15, Issue 3 , December 2021, , Pages 187-197
Abstract
Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and ...
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Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and increase the life expectancy, so researchers have always been looking for ways to improve and accelerate the process of diagnosing this disease. Medical images monitoring and recording the activity of the human heart are the main ways to diagnose heart diseases. Processing of these images is generally complex and time consuming, so scientists and experts have always been looking for ways to speed up and facilitate the detection process. Manifold learning is one of the nonlinear dimension reduction methods which has different algorithms and can simplify the processing of echocardiographic images. In this study, using one of the manifold learning algorithms named LLE, we examined echocardiographic images of the heart, and tried to categorize groups with mitral disorders while identifying healthy data from those with disorders. Results show that the method has carefully separated the data of the healthy group from the group with the disorder, and good results were obtained in the data classification. The results show that more than 80% of the samples of the natural group have a different pattern in terms of manifold structure from the samples with the disorder.
Cognitive Biomedical Engineering
Zahra Soltanifar; Hamid Behnam; Anahita Khorrami Banaraki; Mojtaba Khodadadi; Behnoosh Hamed Ali; Ali Golbazi Mahdipour
Volume 15, Issue 3 , December 2021, , Pages 235-246
Abstract
The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite ...
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The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite of the fact that previous studies on eye dazzling in people with autism are expanding, the findings still do not appear to be consistent. Thus, we tracked eye movements in face processing for 25 teenagers with autism and 25 teenagers from the control group to examine any abnormal concentration in the facial areas. Experimental task used in this study includes standard images of the emotional states of the male and female faces (roundness of the face) in the state of anger, surprise, happiness, sadness and neutrality and subjects looked at these faces, while the eye tracker recorded their eye movements. In this task, they were required to select the displayed emotional state by the reply box. The selected Boosted Trees Ensemble classifier was able to use features related to the total data received from eye tracking in face segmentation into 8 areas (forehead, right and left eye, right and left cheek, nose, mouth and chin) with an accuracy of 83.31% in separating the two groups of autism and control. Moreover, in the study of facial components, left eye, left cheek, right cheek, and right eye, with 84.18%, 83.85%, 82.73% and 81.25% accuracy respectively, were able to make the most difference in the classification. Non-normal patterns in eye gaze can be very important because biomarkers indicate a condition that can be used for early diagnosis. It can also be a guide for researchers to design a game based on the results of this paper to improve the social interactions by strengthening eye contact for people with autism.
Biomedical Image Processing / Medical Image Processing
Parisa Gifani; Hamid Behnam; Maryam Shojaee Fard
Volume 10, Issue 4 , January 2017, , Pages 303-313
Abstract
In this paper, we introduce a novel framework for illustrating the cardiac movements in echocardiogarphic images by utilizing temporal information and sparse representation. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTC) assessed for ...
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In this paper, we introduce a novel framework for illustrating the cardiac movements in echocardiogarphic images by utilizing temporal information and sparse representation. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTC) assessed for each pixel. Then an over complete dictionary based on prior knowledge of the temporal signals and a set of pre-specified known functions was designed. The IVTCs can then be described as linear combinations of a few prototype atoms in the dictionary. We used the Bayesian Compressive Sensing (BCS) sparse recovery algorithm to find the sparse coefficients of the signals. By decomposing the IVTCs to different families and extracting proper features based on the sparse information, we attain the color coded images which illustrates the general movements of cardiac segments. The database consists of 21 echocardiography sequence of normal and abnormal volunteers in short axes and 4 chamber views. The results show the great achievement in global wall motion estimations.
Biomedical Image Processing / Medical Image Processing
Pedram Masaeli; Hamid Behnam; Zahra Alizadeh Sani; Ahmad Shalbaf
Volume 7, Issue 3 , June 2013, , Pages 237-254
Abstract
Coronary artery diseases cause more than half of all deaths in the world. Obviously, early identification is an important way to control coronary artery disease that is diagnosed by measurement and scoring general and regional movement of left ventricle of heart (Normal, Hypokinetic and Akinetic). The ...
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Coronary artery diseases cause more than half of all deaths in the world. Obviously, early identification is an important way to control coronary artery disease that is diagnosed by measurement and scoring general and regional movement of left ventricle of heart (Normal, Hypokinetic and Akinetic). The most common method of imaging the heart using ultrasound is called echocardiography. Using this method accurate view of the heart walls, valves and beginning of main arteries can be obtainbed. Due to the difficulty for the interpretation of these images, time consumption and errors in manual analysis methods, an automated analysis method is required. In this paper we calculate the displacement field in a cycle of heart motion from two-dimensional echocardiography images. To do this, a frame is usually chosen as the reference frame and then all images in a cycle are mapped to it with a mathematical equation. The main idea is to find a semi-local spatiotemporal parametric model for deformation created in a cardiac cycle with nonrigid registration using B-spline functions; as an optimization problem that effectively corrects differences due to movements by minimizing the difference between current frame and a reference frame. Motion estimation accuracy is measured using the sum of squares differences. We use gradient-descend algorithm and multiresolution method to acquire the coefficients in the motion model. The accuracy of the proposed method is assessed using a synthesis sequence of cardiac cycles produced with the simulation software Field II. This algorithm can be applied for the clinical analysis of regional left ventricle then movement parameters and threshold values for the scoring of each section can be extracted. The algorithm represents significant difference between a part of the normal heart and unhealthy heart that shows potential of clinical applications of the proposed method.
Biomedical Image Processing / Medical Image Processing
Fateme Bagheri; Hamid Behnam; Jahangir Tavakoli; Siavash Rahimian
Volume 5, Issue 2 , June 2011, , Pages 117-125
Abstract
In this study we evaluate parameter of nonlinearity and parameter of h by measuring of the amplitude of the second harmonic component and the fundamental component.This method is a variation of the finiteamplitude that has been adopted for pulse echo measurements. We used normal and cooked pork muscle ...
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In this study we evaluate parameter of nonlinearity and parameter of h by measuring of the amplitude of the second harmonic component and the fundamental component.This method is a variation of the finiteamplitude that has been adopted for pulse echo measurements. We used normal and cooked pork muscle in-vitro. For B/A the result is showed as image and for h the result obtain as absolute mean.The result showed that these parameters can distinguish between normal and cooked tissue.This method was considered to be usable for control and monitoring HIFU.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Rashidi; Hamid Behnam; Ali Sheikhani; Mohammad Reza Mohammadi; Maryam Norouzian
Volume 4, Issue 3 , June 2010, , Pages 187-194
Abstract
This paper presents ICA analysis application for detection of autism disorder. In the first step, resources of EEG signals were extracted by ICA and then time domain and frequency domain processing were implemented. EEG signals of ten children with autism and ten healthy children aged 6 to 11 years have ...
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This paper presents ICA analysis application for detection of autism disorder. In the first step, resources of EEG signals were extracted by ICA and then time domain and frequency domain processing were implemented. EEG signals of ten children with autism and ten healthy children aged 6 to 11 years have been obtained. The results have been compared statistically by T-test. Lower correlation levels between resources of the left hemisphere of the brain especially C3 channel region in autistic children compared with healthy subjects have been observed. Also the average energy of theta frequency band in C3 and F3 channels for children with autism were lower than that in healthy people and this criterion was higher in gamma frequency band.
Biomedical Image Processing / Medical Image Processing
Parisa Gifani; Hamid Behnam; Zahra Alizadeh Sani
Volume 4, Issue 2 , June 2010, , Pages 149-160
Abstract
Dimensionality reduction is an important task in machine learning, to simplify data mining, image processing, classification and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Manifold learning is a relatively new approach to nonlinear dimensionality ...
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Dimensionality reduction is an important task in machine learning, to simplify data mining, image processing, classification and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Manifold learning is a relatively new approach to nonlinear dimensionality reduction. Algorithms for manifold learning are based on the intuition that the dimensionality of many data sets may be artificially high and each data point can be described as a function of only a few underlying parameters. Using this tool, intrinsic parameters of the system database, which are main distinction factors of data sets, are recognized and all of them lie on a manifold that shows the real relationship of parameters. One of the successful applications of these methods is in image analysis field. By this approach, each image is a data in high dimensional space that the pixels are its dimensions. Because echocardiography images obtained from a patient are different in quantitative parameters such as heartbeat periodic motion and noise, image sets are reduced to two-dimensional space by a proper manifold learning. In this article, after mapping echocardiography images in two-dimensional space, by using LLE and Isomap algorithms, similar images placed side by side and the relationships between the images according to the cyclic property of heartbeat became evident. The Results showed the weakness of Isomap algorithm and power of LLE algorithm in preserving the relation between consecutive frames. De-noising is an important application which extracted from this research.
Biomedical Image Processing / Medical Image Processing
Hamed Rakhshan; Hamid Behnam
Volume 3, Issue 1 , June 2009, , Pages 25-31
Abstract
Vibroacoustography is a relatively new elasticity imaging method that uses dynamic (oscillatory) radiation force of ultrasound to vibrate the tissue at low frequency (Kilo Hertz). The resulting acoustic emission is recorded with sensitive hydrophone to produce images that are related to the mechanical ...
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Vibroacoustography is a relatively new elasticity imaging method that uses dynamic (oscillatory) radiation force of ultrasound to vibrate the tissue at low frequency (Kilo Hertz). The resulting acoustic emission is recorded with sensitive hydrophone to produce images that are related to the mechanical properties of the tissue. This force is produced by two continuous overlapping ultrasound beams that have a slightly different frequency. Vibroacoustography has been applied to image breast and arteries microcalcification. The lateral resolution of this imaging method is about 0.7mm and its axial resolution is about 12 mm. In this paper two major methods of producing dynamic radiation force, Confocal and X-focal (consists of two concave transducers whose axes cross at their foci at an angle q), are analyzed. A new method for improving axial resolution using short duration pulses is introduced. Simulation results show that we have about 50% improvement in axial resolution using short duration pulses.
