- Jordan
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Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density... more
Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density PD, T1 and T2 that were previously generated from spin-echo sequence. First, the validity of generating synthetic MR images from different sequences were tested and the same PD, T1 and T2 maps that were generated from the real CT image have been used in the simulation of MRI inversion-recovery (IR) sequence. The similarity results between the real and synthetic IR sequence images, using different inversion times TI, showed a very good agreement. After confirming the feasibility of generating synthetic IR images from the PD, T1 and T2-maps, that were originally obtained from spin-echo sequence using the phantom, the simulation of a knee image has been generated from the corresponding knee CT real image using the steady-state transverse magnetization ...
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A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual... more
A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slit-lamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different dimensionality reduction techniques are utilized to uncover the most significant features that give the best results. Experimental results show that manual and automatic feature extraction techniques succeeded in discriminating ulcers from a general grading perspective, with ~93% accuracy using the 30 most significant features extracted using various dimensionality reduction techniques. On the...
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A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual... more
A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slitlamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different dimensionality reduction techniques are utilized to uncover the most significant features that give the best results. Experimental results show that manual and automatic feature extraction techniques succeeded in discriminating ulcers from a general grading perspective, with ~93% accuracy using the 30 most significant features extracted using various dimensionality reduction techniques. On the other hand, automatic deep learning feature extraction discriminated severity grading with a higher accuracy than type grading regardless of the number of features used. To the best of our knowledge, this is the first report to ever attempt to distinguish corneal ulcers based on their grade grading, type grading, ulcer shape, and distribution. Identifying corneal ulcers at an early stage is a preventive measure that reduces aggravation and helps track the efficacy of adapted medical treatment, improving the general public health in remote, underserved areas.
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Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this... more
Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this paper, we design an automated system for 3D kidney segmentation and stones detection in addition to their number and size evaluation. The proposed system is built based on CT kidney image series of 10 subjects, four healthy subjects (with no stones) and the rest have stones based on medical doctor diagnosis, and its performance is tested based on 32 CT kidney series images. The designed system shows its ability to extract kidney either in abdominal or pelvis non-contrast series CT images, and it distinguishes the stones from the surrounding tissues in the kidney image, besides to its ability to analyze the stones and classify them in vivo for further medical treatment. The result agreed with medical doctor's diagnosis. The system can be improved by analyzing the stones in the laboratory and using a large CT dataset. The present method is not limited to extract stones but, also a new approach is proposed to extract the 3D kidneys as well with accuracy 99%.
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The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ... more
The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.
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A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM)... more
A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, whe...
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This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos... more
This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair ...
In general, many university-level students face difficulties in realizing the basic concepts of computed tomography (CT). Didactic computer simulation is preferred to be used in addition to conventional lectures in order to pave the way... more
In general, many university-level students face difficulties in realizing the basic concepts of computed tomography (CT). Didactic computer simulation is preferred to be used in addition to conventional lectures in order to pave the way for better understanding. This paper proposes an educational tool to facilitate the understanding of basic CT concepts which are covered by many biomedical engineering programs around the world, using graphical user interface (GUI) programmed in MATLAB® environment. As well as it is classified to be an educational tool, this GUI can also help researchers to estimate and evaluate the impact of various key parameters in CT experiments. In this paper multi-projection x-ray images were acquired from scanning different samples (synthetic sea horse, fish, beetle and water bottle) using PHYWE XRCT 4.0 CT educational module.
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Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this... more
Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this paper, we design an automated system for 3D kidney segmentation and stones detection in addition to their number and size evaluation. The proposed system is built based on CT kidney image series of 10 subjects, four healthy subjects (with no stones) and the rest have stones based on medical doctor diagnosis, and its performance is tested based on 32 CT kidney series images. The designed system shows its ability to extract kidney either in abdominal or pelvis non-contrast series CT images, and it distinguishes the stones from the surrounding tissues in the kidney image, besides to its ability to analyze the stones and classify them in vivo for further medical treatment. The result agreed with medical doctor's diagnosis. The system can be improved by a...
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In the recently published researches in the object localization field, 3D object localization takes the largest part of this research due to its importance in our daily life. 3D object localization has many applications such as collision... more
In the recently published researches in the object localization field, 3D object localization takes the largest part of this research due to its importance in our daily life. 3D object localization has many applications such as collision avoidance, robotic guiding and vision and object surfaces topography modeling. This research study represents a novel localization algorithm and system design using a low-resolution 2D ultrasonic sensor array for 3D real-time object localization. A novel localization algorithm is developed and applied to the acquired data using the three sensors having the minimum calculated distances at each acquired sample, the algorithm was tested on objects at different locations in 3D space and validated with acceptable level of precision and accuracy. Polytope Faces Pursuit (PFP) algorithm was used for finding an approximate sparse solution to the object location from the measured three minimum distances. The proposed system successfully localizes the object a...
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COVID-19 leads to severe respiratory symptoms that are associated with highly intensive care unit (ICU) admissions and deaths. Early diagnosis of coronavirus limits its wide spreading. Real-time reverse transcription-polymerase chain... more
COVID-19 leads to severe respiratory symptoms that are associated with highly intensive care unit (ICU) admissions and deaths. Early diagnosis of coronavirus limits its wide spreading. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the strategy that has been used by clinicians to discover the presence or absence of this type of virus. This technique has a relatively low positive rate in the early stage of this disease. Therefore, clinicians call for another way to help in the diagnosis of COVID-19. The appearance of X-ray chest images in case of COVID-19 is different from any other type of pneumonic disease. Therefore, this research is devoted to employ artificial intelligence techniques in the early detection of COVID-19 from chest X-ray images. Different hybrid models-each consists of deep features' extraction and classification technique-are implemented to assist clinicians in the detection of COVID-19. Convolutional neural network (CNN) is used to extract the graphical features in the hybrid models' implementations from the chest X-ray images. The classification, to COVID-19 or Non-COVID-19, is achieved using different machine learning algorithms such as CNN, support vector machine (SVM), and random forest (RF) to obtain the best recognition performance. The most significant two extracted features are employed for training and parameters testing. According to the performance results of the designed models, CNN outperforms other classifiers with a testing accuracy of 95.2%.
Research Interests: Electrical Engineering, Computer Science, Biomedical Engineering, Image Processing, Diagnosis & Detection of Mental Illness, and 7 moreDigital Signal Processing, Biomedical signal and image processing, Electronics and biomedical engineering, Digital Image Processing, Artifical Intelligence, X Rays, and Biomedical Sciences
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Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than... more
Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bact...
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Modeling of biomedical signals is crucial and vital for compression, transmission, understanding, feature extraction, and prediction. Researchers devoted their efforts on modeling cardiac related biosignals such as electrocardiography... more
Modeling of biomedical signals is crucial and vital for compression, transmission, understanding, feature extraction, and prediction. Researchers devoted their efforts on modeling cardiac related biosignals such as electrocardiography (ECG), photoplethysmography (PPG), and arterial blood pressure(APP) without concerning other types of non-cardiac related biosignals such as electrooculogram (EOG). This paper focus on the modeling of EOG signal using a novel method that exploits a linear combination of seven gaussian basis functions. The proposed method succeeded on modeling of fifteen different eye movements directions combined from right, left, and center with average root mean squared (RMS) error of I%.
Evidence-based medicine decision-making based on computer-aided methods is a new direction in modernhealthcare. Data Mining Techniques in Computer-Aided Diagnosis (CAD) are powerful and widely used toolsfor efficient and automated... more
Evidence-based medicine decision-making based on computer-aided methods is a new direction in modernhealthcare. Data Mining Techniques in Computer-Aided Diagnosis (CAD) are powerful and widely used toolsfor efficient and automated classification, retrieval, and pattern recognition of medical images. They becomehighly desirable for the healthcare providers because of the massive and increasing volume of intervertebral discdegeneration images. A fast and efficient classification and retrieval system using query images with high degreeof accuracy is vital. The method proposed in this paper for automatic detection and classification of lumbarintervertebral disc degeneration MRI-T2 images makes use of texture-based pattern recognition in data mining.A dataset of 181segmented ROIs, corresponding to 89 normal and 92 degenerated (narrowed) discs at differentvertebral level, was analyzed and textural features (contrast, entropy, and energy) were extracted from each disc-ROI. The extracted fe...
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The diagnosis of cardiac disorders using heart sounds is one of the hottest topics in recent years. In general, diagnosing in the early stage is usually performed using routine auscultation examination using a stethoscope which requires... more
The diagnosis of cardiac disorders using heart sounds is one of the hottest topics in recent years. In general, diagnosing in the early stage is usually performed using routine auscultation examination using a stethoscope which requires human interpretation. Recording of heart sounds using an electronic microphone embedded inside the stethoscope provides a digital recording which is known as a phonocardiogram (PCG). This PCG signal carries very informative data about the status of the heart and its valves. Recently, several machines and deep learning techniques employed signal processing to classify heart disorders using PCG. Based on the used datasets, heart sound can be exploited to classify five types of heart sounds, one is normal, and the others are abnormal and two classes of heart sound, normal and abnormal. This research used a modified version of previously proposed convolutional neural network (CNN) which is AOCTNet architecture for automatic diagnosis of heart valves cond...
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The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ... more
The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to ...
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This research aims to investigate the efficiency of using plexiglass material as an alternative to optical fibers in the production of transparent concrete through studying the mechanical properties of transparent cement mortar. The... more
This research aims to investigate the efficiency of using plexiglass material as an alternative to optical fibers in the production of transparent concrete through studying the mechanical properties of transparent cement mortar. The importance of the current study comes to introduce innovative structural material with high aesthetic and structural specifications and meets the needs of the new concepts of sustainable buildings as well. The novelty of the proposed material lies in innovating a new type of transparent concrete using plexiglass material that allows light to cross through different directions, where such a polymer material can be embedded into the concrete as well-designed distributed bars. The proposed composite performance was evaluated by conducting relevant laboratory experiments on prepared mortar specimens to investigate compressive strength, flexural strength, and bond strength. The plexiglass bars were represented 6% and 16% of the specimen cross-section area, and volume respectively. Experiments were conducted in two time stages, after 7 days and after 28 days of preparing the mortar with plexiglass and fiberglass. Results show that the composite does not harm the mechanical specifications comparing with the conventional mortar, and obtaining new features of concrete with an emphasis on consistency to the requirements of environmental sustainability in the field of architectural construction.
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A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due... more
A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with transfer learning and optimization gradient descent adaptive momentum learning rate (ADAM). The proposed method is applied on Archive International Skin Imaging Collaboration (ISIC) database to classify images into three main classes (benign, melanoma, seborrheic keratosis) under the two scenarios; segmented and non-segmented lesion images. The overall accuracy of the non-segmented classificatio...
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Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with... more
Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 9...