Nourhan Zayed

Electronics Research Institute

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Inas A. Yassine

Cairo University

Doaa Mousa

Electronics Research Institute

Heba A. Elnemr

Electronics Research Institute

M.S. Elsherif

Arab Academy for Science, Technology, and Maritime Transport

Abou‐Bakr M. Youssef

Cairo University

Mahmoud Fakhr

Electronics Research Institute

Yasser M. Kadah

Cairo University

Alaa M. Elsayad

Prince Sattam Bin Abdulaziz University

Bradley G. Goodyear

Foothills Medical Centre

Zeinab A. Mustafa

Sudan University of Science and Technology

Cooperative Institutions

Electronics Research Institute

Cairo University

University of Calgary

Foothills Medical Centre

Nile University

King Abdulaziz University

Gachon University

Ain Shams University

Alberta Health Services

Port Said University

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Is undiagnosed synaesthesia a confounding factor in the interpretation of MRI images?

Nourhan Zayed Brad Goodyear Michael R. Smith

Synaesthesia is a condition in which stimulation of a sensory modality evokes another sensation in the same or a different sensory modality. Currently, synaesthesia is considered a neurological condition that involves crosstalk between brain regions. Given the numerous anatomical and functional connections within the brain, it is possible that undiagnosed synaesthesia may influence the results of functional magnetic resonance imaging (fMRI) studies or even structural MRI. In this paper, we investigated the currently available literature to determine if and how the sensations invoked by synaesthesia could impact fMRI and structural MRI. Our investigation found that synaesthesia can have a profound impact on fMRI studies of sensory and cognitive functions, and there is evidence to suggest structural connections in the brain are also altered. Given the low prevalence of synaesthesia, the likelihood of synaesthesia being a confounding factor in fMRI studies of patient groups is small; however, determining the presence of synaesthesia is important for investigating individual patients.

Sensation

Stimulus modality

Sensory stimulation therapy

Modality (human–computer interaction)

10.1109/iembs.2008.4650527

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Automatic cardiac MRI localization method

Doaa Mousa Nourhan Zayed Inas A. Yassine

Cardiovascular diseases remain the biggest cause of death worldwide. Early detection is the most important step to solve this problem. Cardiac Magnetic Resonance Imaging (CMR) has shown its capability of imaging the heart and evaluation of cardiac function without exposure to ionizing radiation. In this paper, we aim to perform automatic heart localization in MRI short axis view using a simple and effective way. The proposed algorithm can be described through the following two steps: (a) preprocessing step to denoise the images using Gaussian filter, and (b) localization step based on shape recognition technique. The algorithm, implemented using MATLAB, was developed and tested using two datasets. First one consists of 33 subjects normal and abnormal for a total of 7980 2D images and the second one consists of 50 2D images related to normal subject. The results here are evaluated through the classification into three types: totally localized case (complete heart falls inside the selected ROI window), partially localized case (small part, less than 10%, of LV or RV falls outside the selected ROI window), and not localized case (more than 10% of the hearts falls outside the selected ROI window). It gives result with 91.4% accuracy for totally localized case, 8.1% for partially localized case, and 0.5% for not localized case.

Region of interest

Gaussian filter

Window function

10.1109/cibec.2014.7020942

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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays

AI (2025)

Nourhan Zayed Nahed Tawfik Mervat M. A. Mahmoud Ahmed Fawzy Young Im Cho

Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. Their importance resides in their capacity to acquire hierarchical features, which is essential for recognizing complex patterns. Nevertheless, the intricate architectural design of CNNs leads to significant computing requirements. To tackle these issues, it is essential to construct a system based on field-programmable gate arrays (FPGAs) to speed up CNNs. FPGAs provide fast development capabilities, energy efficiency, decreased latency, and advanced reconfigurability. A facial recognition solution by leveraging deep learning and subsequently deploying it on an FPGA platform is suggested. The system detects whether a person has the necessary authorization to enter/access a place. The FPGA is responsible for processing this system with utmost security and without any internet connectivity. Various facial recognition networks are accomplished, including AlexNet, ResNet, and VGG-16 networks. The findings of the proposed method prove that the GoogLeNet network is the best fit due to its lower computational resource requirements, speed, and accuracy. The system was deployed on three hardware kits to appraise the performance of different programming approaches in terms of accuracy, latency, cost, and power consumption. The software programming on the Raspberry Pi-3B kit had a recognition accuracy of around 70–75% and relied on a stable internet connection for processing. This dependency on internet connectivity increases bandwidth consumption and fails to meet the required security criteria, contrary to ZYBO-Z7 board hardware programming. Nevertheless, the hardware/software co-design on the PYNQ-Z2 board achieved an accuracy rate of 85% to 87%. It operates independently of an internet connection, making it a standalone system and saving costs.

Reconfigurability

10.3390/ai6010008

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Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

International Journal of Biomedical Imaging (2015)

Nourhan Zayed Heba A. Elnemr

The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

Abnormality

Texture (cosmology)

10.1155/2015/267807

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Citations (119)

Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data

Scientific Reports (2023)

Doaa Mousa Nourhan Zayed Inas A. Yassine

Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.

10.1038/s41598-023-48693-2

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

Journal of Ambient Intelligence and Humanized Computing (2024)

Ahmed Elrefaiy Nahed Tawfik Nourhan Zayed Ibrahim Elhenawy

Convolution (computer science)

10.1007/s12652-023-04746-y

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Diffusion tensor imaging of optic neuritis and its recovery

Nourhan Zayed Fiona Costello J. Tryfun M. Smith Bradley G. Goodyear

Optic neuritis

Source

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Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY (2016)

Doaa Mousa Nourhan Zayed Inas A. Yassine

Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventriclesâ€™ real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next frame. The second initialization strategy was based on defining the initial contour for each frame using the polar representation of the image. Two short axis view datasets of cardiac magnetic resonance (CMR) perfusion imaging were used in testing the proposed methods. Dice coefficient, sensitivity, specificity and Hausdorff distance have been used to evaluate and validate the segmentation results. The system workflow consists of five main modules: preprocessing, localization, initial contour extraction, registration, and segmentation. The segmentation accuracy for left and right ventricles improved from 72% to 77% and from 70% to 81% using the spatial domain based registration algorithm. The polar-based initialization strategy improves the segmentation accuracy from 77% to 81% and from 81% to 82% for the left and right ventricles respectively.

Initialization

Sørensen–Dice coefficient

Hausdorff distance

10.24297/ijct.v15i11.4376

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Wavelet Domain Bilateral Filtering with Subband Mixing for Magnetic Resonance Image Enhancement

Journal of Medical Imaging and Health Informatics (2012)

Zeinab A. Mustafa Banazier A. Abrahim Inas A. Yassine Nourhan Zayed Yasser M. Kadah

A method for magnetic resonance image denoising based on wavelet domain bilateral filtering (WDBF) is proposed. The main problem in bilateral filtering based methods is that the choice of filtration parameters has a trade-off between preserving edges and noise removal. In this work, a solution that would allow different components of the image to be filtered using different parameters is presented. The bilateral filtering is applied in a customized manner to different wavelet subbands and followed by subband mixing to form the final image. The proposed method is implemented to filter magnetic resonance images and verified both qualitatively and quantitatively. Verification of the new method was carried out on synthetic as well as real data sets. Qualitative and quantitative comparisons with present techniques indicate that the proposed method produces superior denoising results and suggesting potential for clinical application to boost the signal-to-noise ratio of low magnetic field scanners.

10.1166/jmihi.2012.1089

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Skewness of Fractional Anisotropy Detects Decreased White Matter Integrity Resulting From Acute Optic Neuritis

Investigative Ophthalmology & Visual Science (2015)

Bradley G. Goodyear Nourhan Zayed Filomeno Cortese Jessie Trufyn Fiona Costello

Purpose: Diffusion tensor imaging (DTI) has demonstrated optic nerve damage associated with optic neuritis (ON); however, the usefulness of mean fractional anisotropy (FA) specifically is varied in the literature. We wished to determine whether histogram analysis of FA better detects ON damage than mean FA. Methods: The ON patients (n = 24) underwent DTI within 1 month of symptoms and then 6 months later (n = 21). Twelve control subjects participated in one session. Mean FA and axial (AD), radial (RD), and mean (MD) diffusivities were compared between ON and fellow eyes, control eyes, and sessions. Values were sorted into bins, and coefficients of skewness of FA, AD, RD, and MD were compared between ON and fellow eyes, control eyes, and sessions. Results: Mean AD, RD, and MD of ON eyes were significantly reduced compared with fellow eyes (P < 0.04) within 1 month of symptoms, but did not differ at 6 months. Mean AD and RD of ON eyes were significantly lower than control eyes (P < 0.05). No differences were observed for mean FA. The coefficient of skewness of FA was significantly different between ON eyes and fellow eyes (P = 0.03) and control eyes (P = 0.04) within 1 month of symptoms, but did not differ at 6 months. No differences were observed for AD, RD, and MD. Conclusions: Skewness of FA can detect white matter damage associated with ON and its recovery, which may further inform us of how DTI can measure white matter injury and repair.

Optic neuritis

10.1167/iovs.15-17335

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Citations (3)