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Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.
Sayed Haggag; Fahmi Khalifa; Hisham Abdeltawab; Ahmed Elnakib; Mohammed Ghazal; Mohamed A. Mohamed; Harpal Singh Sandhu; Norah Saleh Alghamdi; Ayman El-Baz. An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images. Sensors 2021, 21, 5457 .
AMA StyleSayed Haggag, Fahmi Khalifa, Hisham Abdeltawab, Ahmed Elnakib, Mohammed Ghazal, Mohamed A. Mohamed, Harpal Singh Sandhu, Norah Saleh Alghamdi, Ayman El-Baz. An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images. Sensors. 2021; 21 (16):5457.
Chicago/Turabian StyleSayed Haggag; Fahmi Khalifa; Hisham Abdeltawab; Ahmed Elnakib; Mohammed Ghazal; Mohamed A. Mohamed; Harpal Singh Sandhu; Norah Saleh Alghamdi; Ayman El-Baz. 2021. "An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images." Sensors 21, no. 16: 5457.
Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.
Fatma El-Zahraa A. El-Gamal; Mohammed Elmogy; Ali Mahmoud; Ahmed Shalaby; Andrew E. Switala; Mohammed Ghazal; Hassan Soliman; Ahmed Atwan; Norah Saleh Alghamdi; Gregory Neal Barnes; Ayman El-Baz. A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI). Sensors 2021, 21, 5416 .
AMA StyleFatma El-Zahraa A. El-Gamal, Mohammed Elmogy, Ali Mahmoud, Ahmed Shalaby, Andrew E. Switala, Mohammed Ghazal, Hassan Soliman, Ahmed Atwan, Norah Saleh Alghamdi, Gregory Neal Barnes, Ayman El-Baz. A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI). Sensors. 2021; 21 (16):5416.
Chicago/Turabian StyleFatma El-Zahraa A. El-Gamal; Mohammed Elmogy; Ali Mahmoud; Ahmed Shalaby; Andrew E. Switala; Mohammed Ghazal; Hassan Soliman; Ahmed Atwan; Norah Saleh Alghamdi; Gregory Neal Barnes; Ayman El-Baz. 2021. "A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)." Sensors 21, no. 16: 5416.
Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of
Mohamed Shehata; Ahmed Alksas; Rasha Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Razek; Norah Alghamdi; Ayman El-Baz. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors 2021, 21, 4928 .
AMA StyleMohamed Shehata, Ahmed Alksas, Rasha Abouelkheir, Ahmed Elmahdy, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Hadil Abu Khalifeh, Reem Salim, Ahmed Abdel Razek, Norah Alghamdi, Ayman El-Baz. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors. 2021; 21 (14):4928.
Chicago/Turabian StyleMohamed Shehata; Ahmed Alksas; Rasha Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Razek; Norah Alghamdi; Ayman El-Baz. 2021. "A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors." Sensors 21, no. 14: 4928.
Autism spectrum disorder (ASD) is a neuro-developmental disorder associated with impairments in social and lingual abilities. Failure in language development is variable in the ASD population and follows a wide spectrum. The autism diagnostic observation schedule (ADOS) is the current gold standard for diagnosing plus expert clinical judgment. Currently, studies aim to develop objective computer-aided technologies to diagnose autism with brain imaging modalities and machine learning. Task-based fMRI is a discriminating image modality that measures the functional activation of the brain. Computer-aided diagnosis systems aim to classify autistic subjects against typically developed peers despite the fact that autism is defined over a wide spectrum. Here, we propose a novel computer-aided grading framework in infants and toddlers (between 12 and 40 months) dependent on the analysis of brain activation in response to a speech experiment. First, brain activation responses are analyzed for 157 autistic subjects divided into three groups of: 92 mild, 32 moderate,and 33 severe as defined by ADOS calibrated severity score. Increased hypoactivation of the superior temporal cortex, angular gyrus, primary auditory cortex and cingulate gyri is exhibited with increasing autism spectrum severity. Less lateralization is also present when activation of the left hemisphere regions is recorded. Second, only these region of interest (ROI) areas are included for further local and global feature extraction in our ASD grading system. A comprehensive, two-stage system is developed using different classifiers. Four-fold cross-validation is adopted for testing. The first stage discriminates between moderate and the other two groups with an accuracy of 0.83 (sensitivity = 0.73, specificity = 0.83). Subsequently, a second stage classifies subjects as mild or severe autism with an accuracy of 0.81 (sensitivity = 0.81, specificity = 0.76). Finally, two validation techniques of synthesizing for oversampling and e of multiple random training and testing sets were adopted. The validation results proved the robustness of the proposed framework for an early computer-aided grading system to place subjects on the autism spectrum.
Reem Haweel; Ahmed M. Shalaby; Ali H. Mahmoud; Mohammed Ghazal; Noha Seada; Said Ghoniemy; Manuel Casanova; Gregory N. Barnes; Ayman El-Baz. A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study. IEEE Access 2021, 9, 100570 -100582.
AMA StyleReem Haweel, Ahmed M. Shalaby, Ali H. Mahmoud, Mohammed Ghazal, Noha Seada, Said Ghoniemy, Manuel Casanova, Gregory N. Barnes, Ayman El-Baz. A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study. IEEE Access. 2021; 9 ():100570-100582.
Chicago/Turabian StyleReem Haweel; Ahmed M. Shalaby; Ali H. Mahmoud; Mohammed Ghazal; Noha Seada; Said Ghoniemy; Manuel Casanova; Gregory N. Barnes; Ayman El-Baz. 2021. "A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study." IEEE Access 9, no. : 100570-100582.
Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.
Ahmed Naglah; Fahmi Khalifa; Reem Khaled; Ahmed Abdel Razek; Mohammad Ghazal; Guruprasad Giridharan; Ayman El-Baz. Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN. Sensors 2021, 21, 3878 .
AMA StyleAhmed Naglah, Fahmi Khalifa, Reem Khaled, Ahmed Abdel Razek, Mohammad Ghazal, Guruprasad Giridharan, Ayman El-Baz. Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN. Sensors. 2021; 21 (11):3878.
Chicago/Turabian StyleAhmed Naglah; Fahmi Khalifa; Reem Khaled; Ahmed Abdel Razek; Mohammad Ghazal; Guruprasad Giridharan; Ayman El-Baz. 2021. "Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN." Sensors 21, no. 11: 3878.
The optical coherence tomography angiography (OCTA) is a noninvasive imaging technology which aims at imaging blood vessels in retina by studying decorrelation signals between multiple sequential OCT B-scans captured in the same cross section. Obtaining various vascular plexuses including deep and superficial choriocapillaris, is possible, which helps in understanding the ischemic processes that affect different retina layers. OCTA is a safe imaging modality that does not use dye. OCTA is also fast as it can capture high-resolution images in just seconds. Additionally, it is used in the assessment of structure and blood flow. OCTA provides anatomic details in addition to the vascular flow data. These details are important in understanding the tissue perfusion, specifically, in the absence of apparent morphological change. Using these anatomical details along with perfusion data, OCTA could be used in predicting several ophthalmic diseases. In this paper, we review the OCTA techniques and their ability to detect and diagnose several retinal vascular and optical nerve diseases, such as diabetic retinopathy (DR), anterior ischemic optic neuropathy (AION), age-related macular degeneration (AMD), glaucoma, retinal artery occlusion and retinal vein occlusion. Then, we discuss the main features and disadvantages of using OCTA as a retinal imaging method.
Fatma Taher; Heba Kandil; Hatem Mahmoud; Ali Mahmoud; Ahmed Shalaby; Mohammed Ghazal; Marah Alhalabi; Harpal Sandhu; Ayman El-Baz. A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography. Applied Sciences 2021, 11, 4158 .
AMA StyleFatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Alhalabi, Harpal Sandhu, Ayman El-Baz. A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography. Applied Sciences. 2021; 11 (9):4158.
Chicago/Turabian StyleFatma Taher; Heba Kandil; Hatem Mahmoud; Ali Mahmoud; Ahmed Shalaby; Mohammed Ghazal; Marah Alhalabi; Harpal Sandhu; Ayman El-Baz. 2021. "A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography." Applied Sciences 11, no. 9: 4158.
Photovoltaic (PV) modules comprise bypass diodes to limit hotspot formation. However, they suffer from performance reduction in the presence of partial shading. This paper proposes external circuitry to control the connection type (series/parallel) of the PV cells through a pair of on/off switches resulting in three different operation modes. Mode 1 represents the typical 36 series-connected cells, while mode 2 represents two parallel-connected strings, and mode 3 maximizes the output current where the four strings are connected in parallel. The added values of the approach are that (1) the output current of the PV module can be increased without the need for a buck-boost converter and (2) the partial shading has less impact on the output power than the adoption of bypass diodes. This work shows that simulating three monocrystalline PV modules (120 W, 200 W, and 241 W), consisting of 36, 60, and 72 series-connected cells, lose about 74% when one cell has 80% shading in the absence of bypass diodes. The application of a bypass diode for each pair of strings in the PV module improves this decrease to 61.89%, 40.66%, and 39.47%, respectively. According to our proposed approach, this power loss can be significantly decreased to 19.59%, 50%, and 50.01% for the three PV modules, respectively, representing more than a 42% improvement compared to bypass diodes.
Anas Tarabsheh; Muhammad Akmal; Mohammed Ghazal. Improving the Efficiency of Partially Shaded Photovoltaic Modules without Bypass Diodes. Electronics 2021, 10, 1046 .
AMA StyleAnas Tarabsheh, Muhammad Akmal, Mohammed Ghazal. Improving the Efficiency of Partially Shaded Photovoltaic Modules without Bypass Diodes. Electronics. 2021; 10 (9):1046.
Chicago/Turabian StyleAnas Tarabsheh; Muhammad Akmal; Mohammed Ghazal. 2021. "Improving the Efficiency of Partially Shaded Photovoltaic Modules without Bypass Diodes." Electronics 10, no. 9: 1046.
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.
Sarah Ayyad; Mohamed Shehata; Ahmed Shalaby; Mohamed Abou El-Ghar; Mohammed Ghazal; Moumen El-Melegy; Nahla Abdel-Hamid; Labib Labib; H. Ali; Ayman El-Baz. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors 2021, 21, 2586 .
AMA StyleSarah Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Nahla Abdel-Hamid, Labib Labib, H. Ali, Ayman El-Baz. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors. 2021; 21 (8):2586.
Chicago/Turabian StyleSarah Ayyad; Mohamed Shehata; Ahmed Shalaby; Mohamed Abou El-Ghar; Mohammed Ghazal; Moumen El-Melegy; Nahla Abdel-Hamid; Labib Labib; H. Ali; Ayman El-Baz. 2021. "Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey." Sensors 21, no. 8: 2586.
Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.
Mohamed Shaban; Reem Salim; Hadil Abu Khalifeh; Adel Khelifi; Ahmed Shalaby; Shady El-Mashad; Ali Mahmoud; Mohammed Ghazal; Ayman El-Baz. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors 2021, 21, 2351 .
AMA StyleMohamed Shaban, Reem Salim, Hadil Abu Khalifeh, Adel Khelifi, Ahmed Shalaby, Shady El-Mashad, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors. 2021; 21 (7):2351.
Chicago/Turabian StyleMohamed Shaban; Reem Salim; Hadil Abu Khalifeh; Adel Khelifi; Ahmed Shalaby; Shady El-Mashad; Ali Mahmoud; Mohammed Ghazal; Ayman El-Baz. 2021. "A Deep-Learning Framework for the Detection of Oil Spills from SAR Data." Sensors 21, no. 7: 2351.
Alzheimer’s disease (AD) is a neurodegenerative condition that affects the central nervous system and represents 60% to 70% of all dementia cases. Due to an increased aging population, the number of patients diagnosed with AD is expected to exceed 131 million worldwide by 2050. The disease is characterized by various clinical symptoms and pathological features that define three main sequential decline stages, namely, early/mild, intermediate/moderate and late/severe stages. Although it is considered irreversible, early diagnosis of AD is highly desirable to help preserve cognitive function. However, early diagnosis is difficult due to different factors, including the patient-specific development of AD. The main contribution of the proposed work is to present a personalized (i.e., local/brain regional) computer-aided diagnosis (CAD) system for early diagnosis of AD from two perspectives, functional and structural to assist diagnosis. In other words, the proposed system uniquely yields local/regional diagnosis by combining 11C PiB positron emission tomography (11C PiB PET), which provides functional diagnosis, with structural magnetic resonance imaging (sMRI), which provides structural diagnosis. To the best of our knowledge, this is the first work to combine sMRI and the 11C PiB PET for local/regional early diagnosis of AD. The system processes the two modalities through a number of steps: pre-processing, brain labeling (parcellation), feature extraction, and diagnosis. A local/regional diagnosis is presented for each modality separately, followed by the final global diagnosis obtained by integrating the results from the two modalities. Evaluation of the proposed system shows average results of 97:5%, 100%, and 96:77% for accuracy, specificity, and sensitivity, respectively. With further development, it is envisioned that this system could contribute to the early diagnosis of AD in the clinical setting.
Fatma El-Zahraa A. El-Gamal; Mohammed M. Elmogy; Ashraf Khalil; Mohammed Ghazal; Jawad Yousaf; Xiaolu Qiu; Hassan H. Soliman; Ahmed Atwan; Hermann B. Frieboes; Gregory Neal Barnes; Ayman S. El-Baz. Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis. IEEE Access 2020, 8, 218982 -218996.
AMA StyleFatma El-Zahraa A. El-Gamal, Mohammed M. Elmogy, Ashraf Khalil, Mohammed Ghazal, Jawad Yousaf, Xiaolu Qiu, Hassan H. Soliman, Ahmed Atwan, Hermann B. Frieboes, Gregory Neal Barnes, Ayman S. El-Baz. Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis. IEEE Access. 2020; 8 (99):218982-218996.
Chicago/Turabian StyleFatma El-Zahraa A. El-Gamal; Mohammed M. Elmogy; Ashraf Khalil; Mohammed Ghazal; Jawad Yousaf; Xiaolu Qiu; Hassan H. Soliman; Ahmed Atwan; Hermann B. Frieboes; Gregory Neal Barnes; Ayman S. El-Baz. 2020. "Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis." IEEE Access 8, no. 99: 218982-218996.
Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.
Mohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors 2020, 20, 6348 .
AMA StyleMohammed Ghazal, Tasnim Basmaji, Maha Yaghi, Mohammad Alkhedher, Mohamed Mahmoud, Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors. 2020; 20 (21):6348.
Chicago/Turabian StyleMohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. 2020. "Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things." Sensors 20, no. 21: 6348.
Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder.
Yaser A. Elnakieb; Ahmed Soliman; Ali H. Mahmoud; Mohamed T. Ali; Omar Dekhil; Ahmed M. Shalaby; Mohammed Ghazal; Ashraf Khalil; Andrew Switala; Robert S. Keynton; Gregory Neal Barnes; Ayman El-Baz. Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging. IEEE Access 2020, 8, 1 -1.
AMA StyleYaser A. Elnakieb, Ahmed Soliman, Ali H. Mahmoud, Mohamed T. Ali, Omar Dekhil, Ahmed M. Shalaby, Mohammed Ghazal, Ashraf Khalil, Andrew Switala, Robert S. Keynton, Gregory Neal Barnes, Ayman El-Baz. Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging. IEEE Access. 2020; 8 ():1-1.
Chicago/Turabian StyleYaser A. Elnakieb; Ahmed Soliman; Ali H. Mahmoud; Mohamed T. Ali; Omar Dekhil; Ahmed M. Shalaby; Mohammed Ghazal; Ashraf Khalil; Andrew Switala; Robert S. Keynton; Gregory Neal Barnes; Ayman El-Baz. 2020. "Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging." IEEE Access 8, no. : 1-1.
Autism spectrum disorder (ASD) is a behaviorally diagnosed neurodevelopmental condition of unknown pathology. Research suggests that abnormalities of elecltroencephalogram (EEG) gamma oscillations may provide a biomarker of the condition. In this study, envelope analysis of demodulated waveforms for evoked and induced gamma oscillations in response to Kanizsa figures in an oddball task were analyzed and compared in 19 ASD and 19 age/gender-matched neurotypical children. The ASD group was treated with low frequency transcranial magnetic stimulation (TMS), (1.0 Hz, 90% motor threshold, 18 weekly sessions) targeting the dorsolateral prefrontal cortex. In ASD subjects, as compared to neurotypicals, significant differences in evoked and induced gamma oscillations were evident in higher magnitude of gamma oscillations pre-TMS, especially in response to non-target cues. Recordings post-TMS treatment in ASD revealed a significant reduction of gamma responses to task-irrelevant stimuli. Participants committed fewer errors post-TMS. Behavioral questionnaires showed a decrease in irritability, hyperactivity, and repetitive behavior scores. The use of a novel metric for gamma oscillations. i.e., envelope analysis using wavelet transformation allowed for characterization of the impedance of the originating neuronal circuit. The results suggest that gamma oscillations may provide a biomarker reflective of the excitatory/inhibitory balance of the cortex and a putative outcome measure for interventions in autism.
Manuel F. Casanova; Mohamed Shaban; Mohammed Ghazal; Ayman S. El-Baz; Emily L. Casanova; Ioan Opris; Estate M. Sokhadze. Effects of Transcranial Magnetic Stimulation Therapy on Evoked and Induced Gamma Oscillations in Children with Autism Spectrum Disorder. Brain Sciences 2020, 10, 423 .
AMA StyleManuel F. Casanova, Mohamed Shaban, Mohammed Ghazal, Ayman S. El-Baz, Emily L. Casanova, Ioan Opris, Estate M. Sokhadze. Effects of Transcranial Magnetic Stimulation Therapy on Evoked and Induced Gamma Oscillations in Children with Autism Spectrum Disorder. Brain Sciences. 2020; 10 (7):423.
Chicago/Turabian StyleManuel F. Casanova; Mohamed Shaban; Mohammed Ghazal; Ayman S. El-Baz; Emily L. Casanova; Ioan Opris; Estate M. Sokhadze. 2020. "Effects of Transcranial Magnetic Stimulation Therapy on Evoked and Induced Gamma Oscillations in Children with Autism Spectrum Disorder." Brain Sciences 10, no. 7: 423.
Diabetic retinopathy (DR) is a disease that forms as a complication of diabetes. It is particularly dangerous since it often goes unnoticed and can lead to blindness if not detected early. Despite the clear importance and urgency of such an illness, there is no precise system for the early detection of DR so far. Fortunately, such system could be achieved using deep learning including convolutional neural networks (CNNs), which gained momentum in the field of medical imaging due to its capability of being effectively integrated into various systems in a manner that significantly improves the performance. This paper proposes a computer aided diagnostic (CAD) system for the early detection of non-proliferative DR (NPDR) using CNNs. The proposed system is developed for the optical coherence tomography (OCT) imaging modality. Throughout this paper, all aspects of deployment of the proposed system are studied starting from the preprocessing stage required to extract input retina patches to train the CNN without resizing the image, to the use of transfer learning principals and how to effectively combine features in order to optimize performance. This is done through investigating several scenarios for the system setup and then selecting the best one, which from the results revealed to be a two pre-trained CNNs based system, in which one of these CNNs is independently fed by nasal retina patches and the other one by temporal retina patches. The proposed transfer learning based CAD system achieves a promising accuracy of 94%.
Mohammed Ghazal; Samr Samir Ali; Ali H. Mahmoud; Ahmed M. Shalaby; Ayman El-Baz. Accurate Detection of Non-Proliferative Diabetic Retinopathy in Optical Coherence Tomography Images Using Convolutional Neural Networks. IEEE Access 2020, 8, 34387 -34397.
AMA StyleMohammed Ghazal, Samr Samir Ali, Ali H. Mahmoud, Ahmed M. Shalaby, Ayman El-Baz. Accurate Detection of Non-Proliferative Diabetic Retinopathy in Optical Coherence Tomography Images Using Convolutional Neural Networks. IEEE Access. 2020; 8 ():34387-34397.
Chicago/Turabian StyleMohammed Ghazal; Samr Samir Ali; Ali H. Mahmoud; Ahmed M. Shalaby; Ayman El-Baz. 2020. "Accurate Detection of Non-Proliferative Diabetic Retinopathy in Optical Coherence Tomography Images Using Convolutional Neural Networks." IEEE Access 8, no. : 34387-34397.
Leaf segmentation is significantly important in assisting ecologists to automatically detect symptoms of disease and other stressors affecting trees. This paper employs state-of-the-art techniques in image processing to introduce an accurate framework for segmenting leaves and diseased leaf spots from images. The proposed framework integrates an appearance model that visually represents the current input image with the color prior information generated from RGB color images that were formerly saved in our database. Our framework consists of four main steps: (1) Enhancing the accuracy of the segmentation at minimum time by making use of contrast changes to automatically identify the region of interest (ROI) of the entire leaf, where the pixel-wise intensity relations are described by an electric field energy model. (2) Modeling the visual appearance of the input image using a linear combination of discrete Gaussians (LCDG) to predict the marginal probability distributions of the grayscale ROI main three classes. (3) Calculating the pixel-wise probabilities of these three classes for the color ROI based on the color prior information of database images that are segmented manually, where the current and prior pixel-wise probabilities are used to find the initial labels. (4) Refining the labels with the generalized Gauss-Markov random field model (GGMRF), which maintains the continuity. The proposed segmentation approach was applied to the leaves of mangrove trees in Abu Dhabi in the United Arab Emirates. Experimental validation showed high accuracy, with a Dice similarity coefficient 90% for distinguishing leaf spot from healthy leaf area.
Mohammed Ghazal; Ali Mahmoud; Ahmed Shalaby; Ayman El-Baz. Automated framework for accurate segmentation of leaf images for plant health assessment. Environmental Monitoring and Assessment 2019, 191, 1 -13.
AMA StyleMohammed Ghazal, Ali Mahmoud, Ahmed Shalaby, Ayman El-Baz. Automated framework for accurate segmentation of leaf images for plant health assessment. Environmental Monitoring and Assessment. 2019; 191 (8):1-13.
Chicago/Turabian StyleMohammed Ghazal; Ali Mahmoud; Ahmed Shalaby; Ayman El-Baz. 2019. "Automated framework for accurate segmentation of leaf images for plant health assessment." Environmental Monitoring and Assessment 191, no. 8: 1-13.
A new adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flows (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from MRA data. To accurately separate blood vessels from other regions-of-interest, the marginal distribution is precisely approximated with an adaptive linear combination of the derived model and a number of dominant and subordinate discrete Gaussians, rather than with a mixture of only three pre-selected Gaussian and uniform or Rician components. To validate the accuracy of the proposed algorithm, a special 3D geometrical phantom motivated by statistical analysis of the time-of-flight MRA (TOF-MRA) data is designed. Experiments with synthetic and 50 real data sets confirm the high accuracy and reduced computational cost of the proposed approach.
Ahmed Shalaby; Ali Mahmoud; Mohammed Ghazal; Jasjit S. Suri; Ayman El-Baz. Segmentation of Blood Vessels Using Magnetic Resonance Angiography Images. Cardiovascular Imaging and Image Analysis 2018, 23 -42.
AMA StyleAhmed Shalaby, Ali Mahmoud, Mohammed Ghazal, Jasjit S. Suri, Ayman El-Baz. Segmentation of Blood Vessels Using Magnetic Resonance Angiography Images. Cardiovascular Imaging and Image Analysis. 2018; ():23-42.
Chicago/Turabian StyleAhmed Shalaby; Ali Mahmoud; Mohammed Ghazal; Jasjit S. Suri; Ayman El-Baz. 2018. "Segmentation of Blood Vessels Using Magnetic Resonance Angiography Images." Cardiovascular Imaging and Image Analysis , no. : 23-42.
Blood pressure (BP) changes with age are widespread, and systemic high blood pressure (HBP) is a significant contributor for strokes and cognitive impairment. A non-invasive methodology to quantify changes in cerebral vasculature using Magnetic Resonance Angiography (MRA) imaging and correlation of cerebrovascular changes to mean arterial pressure (MAP) is presented. MRA images and systemic BP measurements were obtained from patients (n = 15, M = 8, F = 7, Age = 49.2 ± 7.3 years) over 700 days (an initial visit and then a follow-up period of 2 years with a final visit). A novel segmentation algorithm was developed to delineate brain vasculature from surrounding tissue. Vascular probability distribution function (PDF) was calculated from segmentation data to correlate the temporal changes in cerebral vasculature to MAP calculated from systemic BP measurements. A 3D reconstruction of the cerebral vasculature was performed using a growing tree model. The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to MAP changes below the circle of Willis (p-value = 0.0007). The proposed methodology may be used to quantify changes in cerebral vasculature and cerebral perfusion pressure non-invasively through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP.
Yitzhak Gebru; Guruprasad Giridharan; Mohammed Ghazal; Ali Mahmoud; Ahmed Shalaby; Ayman El-Baz. Detection of Cerebrovascular Changes Using Magnetic Resonance Angiography. Cardiovascular Imaging and Image Analysis 2018, 1 -22.
AMA StyleYitzhak Gebru, Guruprasad Giridharan, Mohammed Ghazal, Ali Mahmoud, Ahmed Shalaby, Ayman El-Baz. Detection of Cerebrovascular Changes Using Magnetic Resonance Angiography. Cardiovascular Imaging and Image Analysis. 2018; ():1-22.
Chicago/Turabian StyleYitzhak Gebru; Guruprasad Giridharan; Mohammed Ghazal; Ali Mahmoud; Ahmed Shalaby; Ayman El-Baz. 2018. "Detection of Cerebrovascular Changes Using Magnetic Resonance Angiography." Cardiovascular Imaging and Image Analysis , no. : 1-22.
This chapter discusses one of the most critical neurological defects, cerebrovascular diseases and strokes, as they are a leading cause of many serious long-term disabilities. Developing accurate and fast methods for the early diagnosis and detection of potential stroke risk factors is crucial for preventing permanent damage, complications, and ultimately death. One of the most efficient ways for detecting stroke symptoms involves accurate segmentation of cerebrovascular trees and structures. Many modalities have been utilized for this purpose, such as ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Due to many advantages over other modalities, we propose a novel segmentation method based on integration of statistical intensity models with the spatial interaction model for segmentation refinement. Further refinement is achieved by employing Gaussian scale space theory, followed by the majority voting schema and connectivity analysis for obtaining the final 3D segmentation of the cerebrovascular system.
Mohammed Ghazal; Yasmina Al Khalil; Ayman El-Baz. An Unsupervised Parametric Mixture Model for Automatic Cerebrovascular Segmentation. Cardiovascular Imaging and Image Analysis 2018, 95 -108.
AMA StyleMohammed Ghazal, Yasmina Al Khalil, Ayman El-Baz. An Unsupervised Parametric Mixture Model for Automatic Cerebrovascular Segmentation. Cardiovascular Imaging and Image Analysis. 2018; ():95-108.
Chicago/Turabian StyleMohammed Ghazal; Yasmina Al Khalil; Ayman El-Baz. 2018. "An Unsupervised Parametric Mixture Model for Automatic Cerebrovascular Segmentation." Cardiovascular Imaging and Image Analysis , no. : 95-108.
Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.
Mohammad Shehata; Mohammed Ghazal; Garth Beache; Mohamed Abou Ei-Ghar; Amy Dwyer; Hassan Hajjdiab; Ashraf Khalil; Ayman El-Baz. Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants. 2018 25th IEEE International Conference on Image Processing (ICIP) 2018, 146 -150.
AMA StyleMohammad Shehata, Mohammed Ghazal, Garth Beache, Mohamed Abou Ei-Ghar, Amy Dwyer, Hassan Hajjdiab, Ashraf Khalil, Ayman El-Baz. Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants. 2018 25th IEEE International Conference on Image Processing (ICIP). 2018; ():146-150.
Chicago/Turabian StyleMohammad Shehata; Mohammed Ghazal; Garth Beache; Mohamed Abou Ei-Ghar; Amy Dwyer; Hassan Hajjdiab; Ashraf Khalil; Ayman El-Baz. 2018. "Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants." 2018 25th IEEE International Conference on Image Processing (ICIP) , no. : 146-150.
This paper presents a computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at six different b-values. Our system starts by defining a region of interest (ROI) that includes the prostate across the different slices of the input DWI volume. Then, the apparent diffusion coefficient (ADC) of the defined ROI is calculated, normalized and refined. Then, the probability density functions (PDFs) of the refined ADC volumes at the distinct b-values are constructed. Finally, the classification of prostate into either benign or malignant is achieved using a classification system of two stages. The proposed system is the first system of its type that has the ability to detect prostate cancer without any prior processing (e.g., the segmentation of the prostate region). Evaluation of the proposed system is done using DWI datasets acquired from 45 patients (20 benign and 25 malignant) at six distinct b-values. The acquisition of these DWI datasets is performed using two different scanners with distinct magnetic field strengths (1.5 Tesla and 3 Tesla). The resulting area under curve (AUC) is 0.77, which shows that the proposed system approaches the state-of-the-art performance without any prior processing.
Islam Reda; Ahmed Shalaby; Mohammed Elmogy; Mohammed Ghazal; Ahmed Aboulfotouh; Mohamed Abou El-Ghar; Adel Elmaghraby; Robert Keynton; Ayman El-Baz. A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation. 2018 IEEE International Conference on Imaging Systems and Techniques (IST) 2018, 1 -5.
AMA StyleIslam Reda, Ahmed Shalaby, Mohammed Elmogy, Mohammed Ghazal, Ahmed Aboulfotouh, Mohamed Abou El-Ghar, Adel Elmaghraby, Robert Keynton, Ayman El-Baz. A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation. 2018 IEEE International Conference on Imaging Systems and Techniques (IST). 2018; ():1-5.
Chicago/Turabian StyleIslam Reda; Ahmed Shalaby; Mohammed Elmogy; Mohammed Ghazal; Ahmed Aboulfotouh; Mohamed Abou El-Ghar; Adel Elmaghraby; Robert Keynton; Ayman El-Baz. 2018. "A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation." 2018 IEEE International Conference on Imaging Systems and Techniques (IST) , no. : 1-5.