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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Deep Learning and Medical Imaging

IGI Global eBooks (2022)

Nourhan Zayed Heba A. Elnemr

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

10.4018/978-1-6684-7544-7.ch075

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Wavelet segmentation for fetal ultrasound images

Nourhan Zayed A.M. Badwi Alaa M. Elsayad M.S. Elsherif Abou‐Bakr M. Youssef

This paper introduces an efficient algorithm for segmentation of fetal ultrasound images using the multiresolution analysis technique. The proposed algorithm decomposes the input image into a multiresolution space using the B-spline two-dimensional wavelet transform. The system builds features vector for each pixel that contains information about the gray level, moments and other texture information. These vectors are used as inputs for the fuzzy c-means clustering method, which results in a segmented image whose regions are distinct from each other according to texture characteristic content. An Adaptive Center Weighted Median filter is used to enhance fetal ultrasound images before wavelet decomposition. Experiments indicate that this method can be applied with promising results. Preliminary experiments indicate good results in image segmentation while further studies are needed to investigate the potential of wavelet analysis and fuzzy c-means clustering methods as a tool for detecting fetus organs in digital ultrasound images.

10.1109/mwscas.2001.986220

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SIGNIFICANT FEATURES TO DETECT PULMONARY NODULES FROM CT LUNG IMAGES

Biomedical Engineering Applications Basis and Communications (2017)

Doaa Mousa Nourhan Zayed Mahmoud Fakhr

Lung cancer causes the most number of deaths worldwide in both men and women. Early detection and diagnosis can minimize the disease mortality rate. Commonly, chest computed tomography (CT) scans are used by clinicians to diagnose lung cancer. The lung cancer diagnosis relies on detection of the pulmonary nodules in CT scans. In this paper, we propose computer-aided diagnostic systems that can define and suggest the most important features that can distinguish lung nodule from nonnodule one. The proposed system can be described through the following six steps: (a) Patch Extraction, (b) Image Preprocessing, (c) Feature Extraction, (d) Normalization, (e) Feature Reduction, and (f) Patch Classification. Feature extraction and selection are the most important steps in any disease classification process. A combination of 132 texture features with three shape-based features has been extracted. Then the normalization step has been done using min–max method followed by the feature reduction step based on the wrapper approach. The feature reduction step resulted in selecting a set of eight features for the classification process. The algorithm was developed and tested using 166 patches of CT images. The selected eight features achieve accuracy 96.5% using [Formula: see text]-nearest neighbor classifier. The results were validated using the cross-validation technique, [Formula: see text]-fold method.

Normalization

Feature (linguistics)

10.4015/s1016237217500454

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

Doaa Mousa Nourhan Zayed Inas A. Yassine

This paper studies the effect of the registration and initialization of the level set segmentation on the performance of the extracting the heart ventricles for perfusion MRI images. Through the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established on the phase correlation methodology. As for the segmentation experiments, the level set initialization, was done through extracting the ventricles’ real shape from each slice, using threshold and a combination of morphological operations. Though, the final contour of any frame was used as the initial contour for the next frame. This proposed strategy differs from conventional ones in using the real shape of the ventricles as an initial contour than assuming it as circle or ellipse as in the literature. 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 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

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Feature Extraction Techniques

Advances in computational intelligence and robotics book series (2015)

Heba A. Elnemr Nourhan Zayed Mahmoud Abdelmoneim Fakhreldein

The feature extraction is the process to represent raw image in a reduced form to facilitate decision making such as pattern detection, classification or recognition. Finding and extracting reliable and discriminative features is always a crucial step to complete the task of image recognition and computer vision. Furthermore, as the number of application demands increase, an extended study and investigation in the feature extraction field becomes very important. The goal of this chapter is to present an intensive survey of existing literatures on feature extraction techniques over the last years. All these techniques and algorithms have their advantages and limitations. Thus, in this chapter analysis of various techniques and transformations, submitted earlier in literature, for extracting various features from images will be discussed. Additionally, future research directions in the feature extraction area are provided.

Discriminative model

Feature (linguistics)

10.4018/978-1-4666-8654-0.ch013

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Citations (17)

Alzheimer’S Disease Classification Using Bag-Of-Words Based On Visual Pattern Of Diffusion Anisotropy For DTI Imaging

Ghaidaa W. Eldeeb Nourhan Zayed Inas A. Yassine

Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.

Scale-invariant feature transform

10.1109/embc.2018.8512203

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Citations (6)

Wavelet-based segmentation for fetal ultrasound texture images

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2002)

Nourhan Zayed Ahmed M. Badawi Alaa M. Elsayed M.S. Elsherif Abou‐Bakr M. Youssef

This paper introduces an efficient algorithm for segmentation of fetal ultrasound images using the multiresolution analysis technique. The proposed algorithm decomposes the input image into a multiresolution space using the packet two-dimensional wavelet transform. The system builds features vector for each pixel that contains information about the gray level, moments and other texture information. These vectors are used as inputs for the fuzzy c-means clustering method, which results in a segmented image whose regions are distinct from each other according to texture characteristic content. An Adaptive Center Weighted Median filter is used to enhance fetal ultrasound images before wavelet decomposition. Experiments indicate that this method can be applied with promising results. Preliminary experiments indicate good results in image segmentation while further studies are needed to investigate the potential of wavelet analysis and fuzzy c-means clustering methods as a tool for detecting fetus organs in digital ultrasound images.

10.1117/12.467122

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Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T1-weighted magnetic resonance imaging

Scientific Reports (2023)

Shereen Ekhlas Morsy Nourhan Zayed Inas A. Yassine

Abstract Alzheimer’s disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US’s sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T 1 -weighted images to differentiate between Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

Fusion rules

Feature (linguistics)

10.1038/s41598-023-40635-2

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Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

Advances in Bioinformatics (2015)

Fayroz F. Sherif Nourhan Zayed Mahmoud Fakhr

Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.

SNP

Markov blanket

Genome-wide Association Study

Genetic Association

10.1155/2015/639367

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Citations (47)

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

International Journal of Biomedical Imaging (2015)

Eman Magdy Nourhan Zayed Mahmoud Fakhr

Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

10.1155/2015/230830

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Citations (28)