Ahmed Farag Seddik | Helwan University - Faculty of Engineering (original) (raw)

Papers by Ahmed Farag Seddik

Discovering new biological knowledge from the highthroughput biological data is a major challenge... more Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum c...

Journal of Computer Science, 2017

The Electro-Oculogram (EOG) signal can be used for detecting the normality of the eye retina. A n... more The Electro-Oculogram (EOG) signal can be used for detecting the normality of the eye retina. A number of advantages including the flexibility of the recoding process for EOG signals have encouraged insight into EOG based research. This study proposes a new cloud based retinal detachment screening technique based on data mining techniques to diagnose the type of eye retina. The used recognition methods include: back propagation neural network, Kohonen neural network and support vector machine. The obtained results are classified as normal or abnormal eye retina. Given a training set of such patterns, the proposed system is trained how to differentiate a new case in the domain. The diagnosis performance of the proposed systems is evaluated using more than one performance measure such as statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the proposed neural network has achieved a remarkable performance with 100% accuracy on training and test subsets. The main advantage of the proposed system is the high quality of the diagnosis process that help health team to take the suitable decision regarding the patient case. The proposed system may help reducing the cost of screening patients especially in rural areas where experts are not available through sending their data to a central site where the automatic system will help an expert to diagnose the suspected cases.

International Journal of Signal Processing Systems, 2016

The recent technology in digital computing created many ways of drowsiness detection. This is imp... more The recent technology in digital computing created many ways of drowsiness detection. This is important because of the increased numbers of accidents caused by drowsy drivers. In this paper, an approach for detecting drowsiness state by continuously analyzing EEG signals is proposed. Using a single dry-sensor EEG headset, a real-time system that monitors and analyzes the EEG signal of the driver is developed. It automatically produces an alarm to alert the driver via an Android mobile application in case of detecting stage-one sleep. In addition to being portable, the system reached an average accuracy of 97.6% with a low false positive rate in a sample of 60 subjects using the statistical characteristics of the EEG waves. 

Second International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015, 2015

Visually impaired people find difficulties detecting obstacles in front of them, during walking i... more Visually impaired people find difficulties detecting obstacles in front of them, during walking in the street, which makes it dangerous. The smart stick comes as a proposed solution to enable them to identify the world around. In this paper we propose a solution, represented in a smart stick with infrared sensor to detect staircases and pair of ultrasonic sensor to detect any other obstacles in front of the user, within a range of four meters. Moreover, another sensor is placed at the bottom of the stick for the sake of avoiding puddles. Speech warning messages and the vibration motor are activated when any obstacle is detected. This proposed system uses the microcontroller 18F46K80 embedded system, vibration motor and ISD1932 flash memory. The stick is capable of detecting all obstacles in the range 4 meter during 39 ms and gives a suitable respect message empowering blind to move twice his normal speed because she/he feels safe. The smart stick is of low cost, fast response, low power consumption, light weight and ability to fold.

International Journal of Computer Applications, 2017

The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring ... more The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

Journal of Computer Science, 2012

Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (... more Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (MRI) analysis. This study aims to locate the moving heart region from MRI sequence of images. Approach: The idea is to use the motion detection techniques to isolate the heart region from the background image and then apply morphological operations to construct a moving heart region mask. The mask is then applied to the MRI image to separate the Region Of Interest (ROI) that includes the heart. The K-means clustering algorithm is applied to the ROI to segment the heart walls. Results: Experimental results have shown that the performance of the proposed technique is superior to other MRI heart segmentation techniques in both complexity and accuracy. Conclusion: The proposed technique can be used as a pre segmentation step in any other future heart segmentation techniques to increase their accuracy through the localization of the moving heart region. The presented technique is fully automated technique and superior compared to other segmentation techniques.

International Journal of Advanced Computer Science and Applications, 2016

Conventional imaging of diagnostic ultrasound is widely used. Although it makes the differences i... more Conventional imaging of diagnostic ultrasound is widely used. Although it makes the differences in the soft tissues echogenicities' apparent and clear, it fails in describing and estimating the soft tissue mechanical properties. It cannot portray their mechanical properties, such as the elasticity and stiffness. Estimating the mechanical properties increases chances of the identification of lesions or any pathological changes. Physicians are now characterizing the tissue's mechanical properties as diagnostic metrics. Estimating the tissue's mechanical properties is achieved by applying a force on the tissue and calculating the resulted shear wave speed. Due to the difficulty of calculating the shear wave speed precisely inside the tissue, it is estimated by analyzing ultrasound images of the tissue at a very high frame rate. In this paper, the shear wave speed is estimated using finite element analysis. A model is constructed to simulate the tissue's mechanical properties. For a generalized soft tissue model, Agar-gelatine model is used because it has properties similar to that of the soft tissue. A point force is applied at the center of the proposed model. As a result of this force, a deformation is caused. Peak displacements are tracked along the lateral dimension of the model for estimating the shear wave speed of the propagating wave using the Time-To-Peak displacement (TTP) method. Experimental results have shown that the estimated speed of the shear wave is 5.2 m/sec. The speed value is calculated according to shear wave speed equation equals about 5.7 m/sec; this means that our speed estimation system's accuracy is about 91 %, which is reasonable shear wave speed estimation accuracy with a less computational power compared to other tracking methods.

International Journal of Computer Applications, 2016

Early detection of Alzheimer's disease (AD) is important so that preventative measures can be tak... more Early detection of Alzheimer's disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. Alzheimer's disease considered being one of the acute diseases that cause the human death especially in people above 60 years old.

International Journal of Computer Applications, 2016

Cancerous tumors considered being one of the acute diseases that cause the human death especially... more Cancerous tumors considered being one of the acute diseases that cause the human death especially brain cancers. Many computer-aided diagnosis systems are now widely spread to aid in brain tumors diagnosis. Therefore, an automated and reliable computer-aided diagnostic system for diagnosing and classifying the brain tumor has been proposed [1].

Ovarian cancer presents a late clinical stage in more than 80% of the patients and is associated ... more Ovarian cancer presents a late clinical stage in more than 80% of the patients and is associated by a 5-year survival of 35% in this population. Also it is considered to be the 5 th most frequent cancer amongst women. By contrast, the 5-year survival for patients with stage I ovarian cancer exceeds 90%, and most patients are cured of their disease by surgery alone without the need of chemotherapy. Therefore, by increasing the number of detected women in stage I and diagnosing them should have a direct impact on their survival by surgical treatment without the chemotherapy treatment. Hence, new technologies and features selection techniques for the detection of the early stage ovarian cancer are urgently need to give rise to percentage of the survival. Pathological changes within organ might be reflected in proteomic patterns in the blood serum. Therefore these changes appearing in the patterns can be detected to differentiate between the cancerous and normal patients. Here, we aim to select the most significant features that counts for the detection of the ovarian cancer by many features selection techniques, and then evaluating these techniques by calculating the accuracy of each by the aid of classification methods. In this research, we have two serum SELDI (surface-enhanced laser desorption and ionization) mass spectra (MS) datasets to be used to select features amongst them to identify proteomic cancerous serums from normal serums. Features selection techniques have been applied and classification techniques have been applied as well. Amongst the features selection techniques we have chosen to evaluate the performance of PCA (Principal Component Analysis) and GA (Genetic algorithm), and amongst the classification techniques we have chosen the LDA (Linear Discriminant Analysis) and Neural networks so as to evaluate the ability of the selected features in identifying the cancerous patterns. Results were obtained for two combinations of features selection techniques and classification techniques, the first one was PCA+(ttest) technique for features selection and LDA for accuracy tracking yielded an accuracy of 93.0233 % , the other one was genetic algorithm and neural network yielded an accuracy of 100%. So, we conclude that GA is more efficient for features selection and hence for cancerous patterns detection than PCA technique.

2015 Science and Information Conference (SAI), 2015

Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to... more Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to improve the mobility of both blind and visually impaired people. Stick solution use different technologies like ultrasonic, infrared and laser but they still have drawbacks. In this paper we propose, light weight, cheap, user friendly, fast response and low power consumption, smart stick based on infrared technology. A pair of infrared sensors can detect staircases and other obstacles presence in the user path, within a range of two meters. The experimental results achieve good accuracy and the stick is able to detect all of obstacles.

International Journal of Computer Applications, 2015

Diabetic retinopathy is a widespread disease that may cause blindness. Early diagnosis and treatm... more Diabetic retinopathy is a widespread disease that may cause blindness. Early diagnosis and treatment will reduce its side effects and protect the eye. In this paper, a new algorithm for exudates detection is proposed. In the preprocessing step, the green channel of the color image is used, and then median filter followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. The K-means clustering technique is used to select exudates objects. Optic disc is localized using maximum entropy filter and morphological closing. It is demonstrated that combining the K-means with CLAHE of the median filtered image results in 99.39% correct exudates. Experimental results show a reliable and accurate method for segmenting exudates from color retinal images. Performance of the proposed method is evaluated using a set of 52 images from a publicly available dataset STARE.

Journal of Computer Science, 2013

The early detection of cancer is crucial for successful treatment. Medical researchers have inves... more The early detection of cancer is crucial for successful treatment. Medical researchers have investigated a number of early-diagnosis techniques. Recently, they have discovered that some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. Researchers have developed several techniques for the analysis of the mass-spectrum curve analysis and used them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver and colon cancers. In this study we propose a new technique that uses the spectral domain features such as wavelet transform and Fourier transform for the analysis of the ovarian cancer data to differentiate between normal and patients with malignant cancer. We used two different classifiers for the original data, the first one is a feed forward artificial neural network classifier which gave a sensitivity of 96%, specificity of 88% and accuracy of 94%. The second used classifier is the linear discriminant analysis classifier which separated the cancer from healthy samples with sensitivity of 79%, specificity of 75% and accuracy of about 81%. After transforming the data to the spectral domain using the Fourier transform the performance was degraded. The experimental results showed that the performance of the wavelet transform based system was superior to other techniques as it gave a sensitivity of 98%, specificity of 96% and accuracy of 95%.

American Journal of Biomedical Sciences, 2012

Orthopaedic implants and metal implantation are major technological contributions in the field of... more Orthopaedic implants and metal implantation are major technological contributions in the field of orthopaedic surgery. However, bacterial infection and inflammation are predicament issues that subsequently lead to implant failure and second surgery. Ceramic scaffold loaded with gold nanoparticles (Au NPs) posse's antimicrobial and anti-inflammatory properties, which would be more ideal for successful bone implantation and tissue regeneration. Thereby, Hydroxyapatite nanoparticles (nHA), β-Tricalcium Phosphate nanoparticles (nβ-TCP), and Au NPs were used for the fabrication of ceramic scaffold and Au NPs loaded ceramic scaffold. The effects of the Au NPs on the scaffold's mechanical properties, porosity and cell growth have been evaluated. Scanning Electron Microscope [1] and test metric universal testing machine were employed for characterization of the scaffolds. Gold loaded scaffold demonstrated enhanced porosity, degradability and mechanical properties compared with the ceramic scaffold. The porosity of the ceramic and Au NPs loaded ceramic scaffold ranged between 30-50% and 60-75%, respectively, while compressive strength ranged between 10-30mPa and 25-45mPa, respectively.. Scaffold synthesis can be used for implantation in organs that need high load bearing such as femurs, tibia and also as a substrate for Au NPs delivery. To our knowledge, Au NPs have not been incorporate previously with calcium phosphate for fabrication scaffold for bone grafting. Also this study the first report on the effects of Au NPS on the mechanical properties, porosity and degradation rates of ceramic scaffolds.

The main objective of this paper is to predict preterm deliveries at an early gestation period us... more The main objective of this paper is to predict preterm deliveries at an early gestation period using uterine electromyography signals (EMG). Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Previous classification studies use only linear methods as classic spectral analysis to classify the uterine EMG that does not give clinically useful results. On another hand some studies make linear and non-linear analysis for the uterine EMG and find that the non-linear parameters can distinguish the preterm delivery uterine EMG from the term one. In this research, two ways will be taken combining the two previous ideas; the first way is to take some uterine EMG linear parameters as features to a suitable neural network and the second one is to take some uterine EMG non-linear parameters as features to the same neural network. Then, the two ways’ results are compared using ROC analysis which proves that the chance of cor...

American Journal of Biomedical Sciences, 2011

Morphology and mechanical properties of scaffolds seeded with osteoblastes cells used for bone an... more Morphology and mechanical properties of scaffolds seeded with osteoblastes cells used for bone and cartilage repair are the critical factors in bone tissue engineering. In this work, adding CMC and controlling temperature for nano-hydroxyapatite (HA)-b-tricalcium phosphate (b-TCP) scaffold using Polymeric sponge method provide suitable properties. A developed computer system was used to determine properties of scaffold. Porosity, shape and connectivity of pores were analysed based on image processing method. Cells were seeded on scaffold and the differentiation rate was calculated using image analysis. The fabricated sample showed high porosity (nearly 61%) and high compressive strength (nearly 16 MPa), as well as having a well pore size of 200 μm and more. Comparing to Archimedes method, the image result was more accurate. Internal porosity was more than surface porosity due to skin effect.

International Journal of Computer Theory and Engineering, 2011

Feature selection (FS) is a key step in the data mining process. In FS, the objective is to selec... more Feature selection (FS) is a key step in the data mining process. In FS, the objective is to select the smallest subset of features that reduces complexity and ensures generalization. In this paper, we present a combined filter-wrapper feature selection approach using misclassified data. The learning process starts with only one feature, which gives a large number of misclassified patterns. Only these patterns are used to select the next best feature which is added to the first one. By focusing on the misclassified patterns, the learner is undistracted and hence, it can select the relevant features more effectively and faster. The process continues until the classification results are within the required accuracy. The approach is applied to three datasets with high dimensional features using a variety of selection models and search strategies. Experimental results demonstrate the efficiency of the proposed approach in the two-class classification tasks.

Journal of Biomedical Science and Engineering, 2014

Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, a... more Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.

American Journal of Biomedical Engineering, 2012

Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important too... more Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important tool that used for evaluating cardiac function by calculating different cardiac parameters. The manual segmentation of the left ventricle in all image sequences takes a lot of time, and therefore the automatic segmentation of the left ventricle is main step in cardiac function evaluation. In this paper, we proposed an automatic method for segmenting the left ventricle in cardiac MRI images. We applied pixel classification method by using number of features and KNN classifier for segmenting the left ventricle Cavity, and from its output we can get the endocardial contour. Then, we transformed image pixels from Cartesian to polar coordinates for segmenting the epicardial contour. This method was tested on large number of images, and we achieved good results reached to 95.61% sensitivity, and 98.9% specificity for endocardium segmentation, and 93.32% sensitivity, and 98.49% specificity for epicardium segmentation. The results of the proposed method show the availability for fast and reliable segmentation of the left ventricle.

Journal of Biomedical Science and Engineering, 2013

Liver fibrosis recognition is an important issue in diagnostic imaging. The accurate estimation o... more Liver fibrosis recognition is an important issue in diagnostic imaging. The accurate estimation of liver fibrosis stages is important to establish prognosis and to guide appropriate treatment decisions. Liver biopsy has been for many years the reference procedure to assess histological definition for liver diseases. But biopsy measurement is an invasive method besides it takes large time. So, fast and improved methods are needed. Using elastography technology, a correlation technique can be used to calculate the displacement of liver tissue after it has suffered a compression force. This displacement is related to tissue stiffness, and liver fibrosis can be classified into stages according to that displacement. The value of compression force affects the displacement of tissue and so affects the results of the liver fibrosis diagnosing. By using finite element method, liver fibrosis can be recognized directly within a short time. The proposed work succeeded in recognizing liver fibrosis by a percent reached in average to 86.67% on a simulation environment.

Discovering new biological knowledge from the highthroughput biological data is a major challenge... more Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum c...

Journal of Computer Science, 2017

The Electro-Oculogram (EOG) signal can be used for detecting the normality of the eye retina. A n... more The Electro-Oculogram (EOG) signal can be used for detecting the normality of the eye retina. A number of advantages including the flexibility of the recoding process for EOG signals have encouraged insight into EOG based research. This study proposes a new cloud based retinal detachment screening technique based on data mining techniques to diagnose the type of eye retina. The used recognition methods include: back propagation neural network, Kohonen neural network and support vector machine. The obtained results are classified as normal or abnormal eye retina. Given a training set of such patterns, the proposed system is trained how to differentiate a new case in the domain. The diagnosis performance of the proposed systems is evaluated using more than one performance measure such as statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the proposed neural network has achieved a remarkable performance with 100% accuracy on training and test subsets. The main advantage of the proposed system is the high quality of the diagnosis process that help health team to take the suitable decision regarding the patient case. The proposed system may help reducing the cost of screening patients especially in rural areas where experts are not available through sending their data to a central site where the automatic system will help an expert to diagnose the suspected cases.

International Journal of Signal Processing Systems, 2016

The recent technology in digital computing created many ways of drowsiness detection. This is imp... more The recent technology in digital computing created many ways of drowsiness detection. This is important because of the increased numbers of accidents caused by drowsy drivers. In this paper, an approach for detecting drowsiness state by continuously analyzing EEG signals is proposed. Using a single dry-sensor EEG headset, a real-time system that monitors and analyzes the EEG signal of the driver is developed. It automatically produces an alarm to alert the driver via an Android mobile application in case of detecting stage-one sleep. In addition to being portable, the system reached an average accuracy of 97.6% with a low false positive rate in a sample of 60 subjects using the statistical characteristics of the EEG waves. 

Second International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015, 2015

Visually impaired people find difficulties detecting obstacles in front of them, during walking i... more Visually impaired people find difficulties detecting obstacles in front of them, during walking in the street, which makes it dangerous. The smart stick comes as a proposed solution to enable them to identify the world around. In this paper we propose a solution, represented in a smart stick with infrared sensor to detect staircases and pair of ultrasonic sensor to detect any other obstacles in front of the user, within a range of four meters. Moreover, another sensor is placed at the bottom of the stick for the sake of avoiding puddles. Speech warning messages and the vibration motor are activated when any obstacle is detected. This proposed system uses the microcontroller 18F46K80 embedded system, vibration motor and ISD1932 flash memory. The stick is capable of detecting all obstacles in the range 4 meter during 39 ms and gives a suitable respect message empowering blind to move twice his normal speed because she/he feels safe. The smart stick is of low cost, fast response, low power consumption, light weight and ability to fold.

International Journal of Computer Applications, 2017

The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring ... more The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

Journal of Computer Science, 2012

Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (... more Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (MRI) analysis. This study aims to locate the moving heart region from MRI sequence of images. Approach: The idea is to use the motion detection techniques to isolate the heart region from the background image and then apply morphological operations to construct a moving heart region mask. The mask is then applied to the MRI image to separate the Region Of Interest (ROI) that includes the heart. The K-means clustering algorithm is applied to the ROI to segment the heart walls. Results: Experimental results have shown that the performance of the proposed technique is superior to other MRI heart segmentation techniques in both complexity and accuracy. Conclusion: The proposed technique can be used as a pre segmentation step in any other future heart segmentation techniques to increase their accuracy through the localization of the moving heart region. The presented technique is fully automated technique and superior compared to other segmentation techniques.

International Journal of Advanced Computer Science and Applications, 2016

Conventional imaging of diagnostic ultrasound is widely used. Although it makes the differences i... more Conventional imaging of diagnostic ultrasound is widely used. Although it makes the differences in the soft tissues echogenicities' apparent and clear, it fails in describing and estimating the soft tissue mechanical properties. It cannot portray their mechanical properties, such as the elasticity and stiffness. Estimating the mechanical properties increases chances of the identification of lesions or any pathological changes. Physicians are now characterizing the tissue's mechanical properties as diagnostic metrics. Estimating the tissue's mechanical properties is achieved by applying a force on the tissue and calculating the resulted shear wave speed. Due to the difficulty of calculating the shear wave speed precisely inside the tissue, it is estimated by analyzing ultrasound images of the tissue at a very high frame rate. In this paper, the shear wave speed is estimated using finite element analysis. A model is constructed to simulate the tissue's mechanical properties. For a generalized soft tissue model, Agar-gelatine model is used because it has properties similar to that of the soft tissue. A point force is applied at the center of the proposed model. As a result of this force, a deformation is caused. Peak displacements are tracked along the lateral dimension of the model for estimating the shear wave speed of the propagating wave using the Time-To-Peak displacement (TTP) method. Experimental results have shown that the estimated speed of the shear wave is 5.2 m/sec. The speed value is calculated according to shear wave speed equation equals about 5.7 m/sec; this means that our speed estimation system's accuracy is about 91 %, which is reasonable shear wave speed estimation accuracy with a less computational power compared to other tracking methods.

International Journal of Computer Applications, 2016

Early detection of Alzheimer's disease (AD) is important so that preventative measures can be tak... more Early detection of Alzheimer's disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. Alzheimer's disease considered being one of the acute diseases that cause the human death especially in people above 60 years old.

International Journal of Computer Applications, 2016

Cancerous tumors considered being one of the acute diseases that cause the human death especially... more Cancerous tumors considered being one of the acute diseases that cause the human death especially brain cancers. Many computer-aided diagnosis systems are now widely spread to aid in brain tumors diagnosis. Therefore, an automated and reliable computer-aided diagnostic system for diagnosing and classifying the brain tumor has been proposed [1].

Ovarian cancer presents a late clinical stage in more than 80% of the patients and is associated ... more Ovarian cancer presents a late clinical stage in more than 80% of the patients and is associated by a 5-year survival of 35% in this population. Also it is considered to be the 5 th most frequent cancer amongst women. By contrast, the 5-year survival for patients with stage I ovarian cancer exceeds 90%, and most patients are cured of their disease by surgery alone without the need of chemotherapy. Therefore, by increasing the number of detected women in stage I and diagnosing them should have a direct impact on their survival by surgical treatment without the chemotherapy treatment. Hence, new technologies and features selection techniques for the detection of the early stage ovarian cancer are urgently need to give rise to percentage of the survival. Pathological changes within organ might be reflected in proteomic patterns in the blood serum. Therefore these changes appearing in the patterns can be detected to differentiate between the cancerous and normal patients. Here, we aim to select the most significant features that counts for the detection of the ovarian cancer by many features selection techniques, and then evaluating these techniques by calculating the accuracy of each by the aid of classification methods. In this research, we have two serum SELDI (surface-enhanced laser desorption and ionization) mass spectra (MS) datasets to be used to select features amongst them to identify proteomic cancerous serums from normal serums. Features selection techniques have been applied and classification techniques have been applied as well. Amongst the features selection techniques we have chosen to evaluate the performance of PCA (Principal Component Analysis) and GA (Genetic algorithm), and amongst the classification techniques we have chosen the LDA (Linear Discriminant Analysis) and Neural networks so as to evaluate the ability of the selected features in identifying the cancerous patterns. Results were obtained for two combinations of features selection techniques and classification techniques, the first one was PCA+(ttest) technique for features selection and LDA for accuracy tracking yielded an accuracy of 93.0233 % , the other one was genetic algorithm and neural network yielded an accuracy of 100%. So, we conclude that GA is more efficient for features selection and hence for cancerous patterns detection than PCA technique.

2015 Science and Information Conference (SAI), 2015

Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to... more Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to improve the mobility of both blind and visually impaired people. Stick solution use different technologies like ultrasonic, infrared and laser but they still have drawbacks. In this paper we propose, light weight, cheap, user friendly, fast response and low power consumption, smart stick based on infrared technology. A pair of infrared sensors can detect staircases and other obstacles presence in the user path, within a range of two meters. The experimental results achieve good accuracy and the stick is able to detect all of obstacles.

International Journal of Computer Applications, 2015

Diabetic retinopathy is a widespread disease that may cause blindness. Early diagnosis and treatm... more Diabetic retinopathy is a widespread disease that may cause blindness. Early diagnosis and treatment will reduce its side effects and protect the eye. In this paper, a new algorithm for exudates detection is proposed. In the preprocessing step, the green channel of the color image is used, and then median filter followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. The K-means clustering technique is used to select exudates objects. Optic disc is localized using maximum entropy filter and morphological closing. It is demonstrated that combining the K-means with CLAHE of the median filtered image results in 99.39% correct exudates. Experimental results show a reliable and accurate method for segmenting exudates from color retinal images. Performance of the proposed method is evaluated using a set of 52 images from a publicly available dataset STARE.

Journal of Computer Science, 2013

The early detection of cancer is crucial for successful treatment. Medical researchers have inves... more The early detection of cancer is crucial for successful treatment. Medical researchers have investigated a number of early-diagnosis techniques. Recently, they have discovered that some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. Researchers have developed several techniques for the analysis of the mass-spectrum curve analysis and used them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver and colon cancers. In this study we propose a new technique that uses the spectral domain features such as wavelet transform and Fourier transform for the analysis of the ovarian cancer data to differentiate between normal and patients with malignant cancer. We used two different classifiers for the original data, the first one is a feed forward artificial neural network classifier which gave a sensitivity of 96%, specificity of 88% and accuracy of 94%. The second used classifier is the linear discriminant analysis classifier which separated the cancer from healthy samples with sensitivity of 79%, specificity of 75% and accuracy of about 81%. After transforming the data to the spectral domain using the Fourier transform the performance was degraded. The experimental results showed that the performance of the wavelet transform based system was superior to other techniques as it gave a sensitivity of 98%, specificity of 96% and accuracy of 95%.

American Journal of Biomedical Sciences, 2012

Orthopaedic implants and metal implantation are major technological contributions in the field of... more Orthopaedic implants and metal implantation are major technological contributions in the field of orthopaedic surgery. However, bacterial infection and inflammation are predicament issues that subsequently lead to implant failure and second surgery. Ceramic scaffold loaded with gold nanoparticles (Au NPs) posse's antimicrobial and anti-inflammatory properties, which would be more ideal for successful bone implantation and tissue regeneration. Thereby, Hydroxyapatite nanoparticles (nHA), β-Tricalcium Phosphate nanoparticles (nβ-TCP), and Au NPs were used for the fabrication of ceramic scaffold and Au NPs loaded ceramic scaffold. The effects of the Au NPs on the scaffold's mechanical properties, porosity and cell growth have been evaluated. Scanning Electron Microscope [1] and test metric universal testing machine were employed for characterization of the scaffolds. Gold loaded scaffold demonstrated enhanced porosity, degradability and mechanical properties compared with the ceramic scaffold. The porosity of the ceramic and Au NPs loaded ceramic scaffold ranged between 30-50% and 60-75%, respectively, while compressive strength ranged between 10-30mPa and 25-45mPa, respectively.. Scaffold synthesis can be used for implantation in organs that need high load bearing such as femurs, tibia and also as a substrate for Au NPs delivery. To our knowledge, Au NPs have not been incorporate previously with calcium phosphate for fabrication scaffold for bone grafting. Also this study the first report on the effects of Au NPS on the mechanical properties, porosity and degradation rates of ceramic scaffolds.

The main objective of this paper is to predict preterm deliveries at an early gestation period us... more The main objective of this paper is to predict preterm deliveries at an early gestation period using uterine electromyography signals (EMG). Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Previous classification studies use only linear methods as classic spectral analysis to classify the uterine EMG that does not give clinically useful results. On another hand some studies make linear and non-linear analysis for the uterine EMG and find that the non-linear parameters can distinguish the preterm delivery uterine EMG from the term one. In this research, two ways will be taken combining the two previous ideas; the first way is to take some uterine EMG linear parameters as features to a suitable neural network and the second one is to take some uterine EMG non-linear parameters as features to the same neural network. Then, the two ways’ results are compared using ROC analysis which proves that the chance of cor...

American Journal of Biomedical Sciences, 2011

Morphology and mechanical properties of scaffolds seeded with osteoblastes cells used for bone an... more Morphology and mechanical properties of scaffolds seeded with osteoblastes cells used for bone and cartilage repair are the critical factors in bone tissue engineering. In this work, adding CMC and controlling temperature for nano-hydroxyapatite (HA)-b-tricalcium phosphate (b-TCP) scaffold using Polymeric sponge method provide suitable properties. A developed computer system was used to determine properties of scaffold. Porosity, shape and connectivity of pores were analysed based on image processing method. Cells were seeded on scaffold and the differentiation rate was calculated using image analysis. The fabricated sample showed high porosity (nearly 61%) and high compressive strength (nearly 16 MPa), as well as having a well pore size of 200 μm and more. Comparing to Archimedes method, the image result was more accurate. Internal porosity was more than surface porosity due to skin effect.

International Journal of Computer Theory and Engineering, 2011

Feature selection (FS) is a key step in the data mining process. In FS, the objective is to selec... more Feature selection (FS) is a key step in the data mining process. In FS, the objective is to select the smallest subset of features that reduces complexity and ensures generalization. In this paper, we present a combined filter-wrapper feature selection approach using misclassified data. The learning process starts with only one feature, which gives a large number of misclassified patterns. Only these patterns are used to select the next best feature which is added to the first one. By focusing on the misclassified patterns, the learner is undistracted and hence, it can select the relevant features more effectively and faster. The process continues until the classification results are within the required accuracy. The approach is applied to three datasets with high dimensional features using a variety of selection models and search strategies. Experimental results demonstrate the efficiency of the proposed approach in the two-class classification tasks.

Journal of Biomedical Science and Engineering, 2014

Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, a... more Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.

American Journal of Biomedical Engineering, 2012

Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important too... more Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important tool that used for evaluating cardiac function by calculating different cardiac parameters. The manual segmentation of the left ventricle in all image sequences takes a lot of time, and therefore the automatic segmentation of the left ventricle is main step in cardiac function evaluation. In this paper, we proposed an automatic method for segmenting the left ventricle in cardiac MRI images. We applied pixel classification method by using number of features and KNN classifier for segmenting the left ventricle Cavity, and from its output we can get the endocardial contour. Then, we transformed image pixels from Cartesian to polar coordinates for segmenting the epicardial contour. This method was tested on large number of images, and we achieved good results reached to 95.61% sensitivity, and 98.9% specificity for endocardium segmentation, and 93.32% sensitivity, and 98.49% specificity for epicardium segmentation. The results of the proposed method show the availability for fast and reliable segmentation of the left ventricle.

Journal of Biomedical Science and Engineering, 2013

Liver fibrosis recognition is an important issue in diagnostic imaging. The accurate estimation o... more Liver fibrosis recognition is an important issue in diagnostic imaging. The accurate estimation of liver fibrosis stages is important to establish prognosis and to guide appropriate treatment decisions. Liver biopsy has been for many years the reference procedure to assess histological definition for liver diseases. But biopsy measurement is an invasive method besides it takes large time. So, fast and improved methods are needed. Using elastography technology, a correlation technique can be used to calculate the displacement of liver tissue after it has suffered a compression force. This displacement is related to tissue stiffness, and liver fibrosis can be classified into stages according to that displacement. The value of compression force affects the displacement of tissue and so affects the results of the liver fibrosis diagnosing. By using finite element method, liver fibrosis can be recognized directly within a short time. The proposed work succeeded in recognizing liver fibrosis by a percent reached in average to 86.67% on a simulation environment.