Yasser Mostafa Kadah

Prof. Yasser Mostafa Kadah

orcid

Research Focus & Metrics

Academic Career Length

34Years (1993-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 November 2002 in IEEE Transactions on Biomedical Engineering
Verified
Journal Article
Closed access
KAK.Z. Abd-Elmoniem
K.Z. Abd-Elmoniem
AYA.-B.M. Youssef
A.-B.M. Youssef
YKYasser Mostafa Kadah
Yasser Mostafa Kadah
This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.

1 August 1996 in IEEE Transactions on Medical Imaging
Verified
Journal Article
Closed access
YKYasser Mostafa Kadah
Yasser Mostafa Kadah
AFA.A. Farag
A.A. Farag
JZJ.M. Zurada
J.M. Zurada
ABA.M. Badawi
A.M. Badawi
AYA.-B.M. Youssef
A.-B.M. Youssef
Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.

7 August 2002 in IEEE Transactions on Biomedical Engineering
Verified
Journal Article
Closed access
MOM.I. Owis
M.I. Owis
AAA.H. Abou-Zied
A.H. Abou-Zied
AYA.-B.M. Youssef
A.-B.M. Youssef
YKYasser Mostafa Kadah
Yasser Mostafa Kadah
We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.

25 September 2017 in Journal of Medical and Biological Engineering
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Journal Article
Closed access
PDF file
Mugahed A. Al-antariMugahed A. Al-antari
Mugahed A. Al-antari
Mohammed Al-masniMohammed Al-masni
Mohammed Al-masni
SPSung-Un Park
Sung-Un Park
JPJunHyeok Park
JunHyeok Park
MMMohamed K. Metwally
Mohamed K. Metwally
TKTae-Seong Kim
Tae-Seong Kim

1 March 2020 in Biomedical Signal Processing and Control
Verified
Journal Article
Closed access
ZAZaid Abduh
Zaid Abduh
ENEbrahim Ameen Nehary
Ebrahim Ameen Nehary
MWManal Abdel Wahed
Manal Abdel Wahed
YKYasser Mostafa Kadah
Yasser Mostafa Kadah

1 October 1997 in Magnetic Resonance in Medicine
Verified
Journal Article
Closed access
PDF file
YKYasser Mostafa Kadah
Yasser Mostafa Kadah
XHXiaoping Hu
Xiaoping Hu