Ayman El-Baz, Ph.D.

Education:Ayman El-Baz, Ph.D. 

B.S., Electronics and Communications Engineering, Mansoura University, Mansoura, Egypt; 1997.
M.S., Electronics and Communications Engineering, Mansoura University, Mansoura, Egypt; 2001.
Ph.D., Electrical and Computer Engineering, University of Louisville, Louisville, KY; 2006.

Curriculum Vitae

Current Positions:

Chair, Department of Bioengineering, Speed School of Engineering, University of Louisville.
Professor – Tenured, Department of Bioengineering, Speed School of Engineering, University of Louisville.
Director, BioImaging Laboratory, Department of Bioengineering, Speed School of Engineering, University of Louisville.
Associate Faculty – Professor, Department of Pharmacology & Toxicology, School of Medicine, University of Louisville.
Associate Faculty – Professor, Department of Computer Engineering & Computer Science, Speed School of Engineering, University of Louisville.
Associate Faculty – Professor, Department of Psychiatry & Behavioral Sciences, School of Medicine, University of Louisville.
Associate Faculty – Professor, Department of Electrical & Computer Engineering, Speed School of Engineering, University of Louisville.
Associate Faculty – Associate Professor, Department of Pharmacology & Toxicology, School of Medicine, University of Louisville.
Associate Faculty – Associate Professor, Department of Computer Engineering & Computer Science, Speed School of Engineering, University of Louisville.
Associate Faculty – Associate Professor, Department of Psychiatry & Behavioral Sciences, School of Medicine, University of Louisville.
Associate Faculty – Associate Professor, Department of Electrical & Computer Engineering, Speed School of Engineering, University of Louisville.

Contact Information: 

Lutz Hall Building
University of Louisville
200 E Shipp Ave
Louisville, KY 40208, USA
Phone 502-852-5092
Fax 502-852-1577

Email: aselba01@louisville.edu

Website:

http://louisville.edu/speed/people/faculty/elBazAyman

Research Description:

Dr. El-Baz has 14-years of hands-on experience in the fields of biosignaling, bioimaging modeling and computer assisted functional diagnostic diagnosis systems, including those using CT, MRI, EMG, ECG, EEG and other physiological signals. He has developed new techniques for accurate identification of probability mixtures for segmenting multi-modal images, new probability models and model-based algorithms as well as new registration techniques based on multiple second-order signal statistics.

Also, he has developed a new non-invasive CAD system for the automatic detection of acute renal rejection after kidney transplantation, using Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) and Diffusion Weighted Images (DWI). The innovation in this work is a comprehensive noninvasive CAD system that optimally characterizes the status of the transplanted kidney based on the fusion of stochastic approaches using new MGRF energy models and geometric approaches.

Moreover, Dr. El-Baz has developed a software system to eliminate the ECG artifacts from EMG recordings. The developed approach has the ability to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub- wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root- mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wave- let-based filtering has the benefit of not introducing error in the native EMG signal and accurately re- moving ECG artifacts from EMG signals.

In addition, he has developed a novel automatic CAD system for the early detection and diagnosis of lung cancer based on estimating new indices (e.g., a shape index, an appearance index, and a growth rate index) from spiral low dose computed tomography images. This system is able to quickly diagnose small, malignant lung nodules at an early stage as well as large nodules located more than 4 cm away from large diameter airways, cases which the current technology, i.e. needle biopsy and bronchoscopy, fail to diagnose.

Dr. El-Baz also has developed a novel CAD system for the early diagnosis of autism using Magnetic Resonance (MR) images. This CAD system is based on distinguishing autistic brains from normally developed brains based on our new findings that the thickness of the cerebral white matter gyrifications of autistic subjects are thinner than the thickness of CWM matter gyrifications of normal brains.  The innovation in this study is based on using Spherical Harmonics (SHs) decomposition to quantify shape discrepancies in the brain cortex in an effort to extract features that distinguish autistic patients from controls. Moreover, he has developed a new MRI-based non-invasive CAD system for early detection of Dyslexia. All of these contributions have been reported at several international conferences and journals.

Literature Cited:

Detection of Acute Renal Rejection

  1. Shehata M, Khalifa F, Soliman A, Ghazal M, Abou El-Ghar M, Dwyer A, Gimel’farb G, Keynton R, El-Baz A.  Computer-aided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI.  IEEE Transactions on Biomedical Engineering 2018 Jun 25. PMID: 29993503.
  2. Shehata M, Mahmoud A, Soliman A, Khalifa F, Ghazal M, Abou El-Ghar M, El-Melegy M, El-Baz, A.  3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary.  PLoS One 2018 Jul 13;13(7):0200082. eCollection 2018. PMID: 30005069. PMCID: PMC6044527.
  3. Khalifa F, Shehata M, Soliman A, Abou El-Ghar M, El-Diasty T, Dwyer AC, El-Melegy M, Gimel’farb G, Keynton R, El-Baz A.  A generalized MRI-based CAD system for functional assessment of renal transplant.  International Symposium on Biomedical Imaging 2017;14:758–61.
  4. Shehata M, Khalifa F, Soliman A, Abou El-Ghar M, Dwyer A, Gimel’farb G, Keynton R, El-Baz A.  A promising non-invasive CAD system for kidney function assessment.  Lecture Notes in Computer Science 2016;9902:613-21.
  5. Khalifa F, Soliman A, Dwyer A, Gimel’farb G. El-Baz A.  A Random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images.  IEEE International Conference on Pattern Recognition 2016;23:3399–403.
  6. Shehata M, Khalifa F, Soliman A, Takieldeen A, Abou El-Ghar M, Shaffie A, Dwyer AC, Ouseph R, El-Baz A, Keynton R.  3D diffusion MRI-based CAD system for early diagnosis of acute renal rejection.  International Symposium on Biomedical Imaging 2016;131177-80. doi: 10.1109/ISBI.2016.7493476.

Detection of Lung Cancer

  1. Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, Gimel’farb G, Keynton R, El-Baz A.  Accurate lungs segmentation on CT chest images by adaptive appearance guided shape modeling.  IEEE Transactions of Medical Imaging 2017 Jan;36(1):263-76. PMID: 27705854.
  2. El-Baz A, Suri J, eds.  Lung Imaging and Computer Aided Diagnosis. Boca Raton: CRC Press, 2018.
  3. El-Baz A, Elnakib A, Abou El-Ghar M, Gimel’farb G, Falk R, Farag A.  Automatic detection of 2D and 3D lung nodules in chest spiral CT scans.  International Journal of Biomedical Imaging 2013;2013:517632.  PMID: 23509444. PMCID: PMC3590446.
  4. El-Baz A, Beache G, Gimel’farb G, Suzuki K, Okada K, Elnakib A, Soliman A, Abdollahi B.  Computer aided diagnosis systems for lung cancer: challenges and methodologies.  International Journal of Biomedical Imaging 2013;2013:942353. PMID: 23431282. PMCID: PMC3570946.
  5. El-Baz A, Sethu P, Gimel’farb G, Khalifa F, Elnakibj A, Falk R, Abu El-Ghar M.  Elastic phantoms generated by microfluidics technology: validation of an imaged-based approach for accurate measurement of the growth rate of lung nodules.  Biotechnology Journal 2011 Feb;6(2):195-203.  PMID: 21298804.

Early Diagnosis of Autism

  1. Dekhil O, ElNakieb Y, ElShamekh A, Shalaby A, Ayindey B, Mahmoud A, Switala A, Elmaghraby A, Keynton R, Ghazal M, Barnes G, El-Baz A.  Identifying personalized autism related impairments using resting functional MRI and ADOS reports.  International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018.
  2. Casanova M, El-Baz A, Suri J, eds.  Imaging the brain in autism.  New York: Springer, 2013.
  3. Casanova M, El-Baz A, Kamat S, Dombroski B, Khalifa F, Elnakib A, Soliman A, Allison-McNutt A, Switala A.  Focal cortical dysplasias in autism spectrum disorders.  Acta Neuropathologica Communications 2013 Oct 11;1(1):67.  PMID: 24252498. PMCID: PMC3893372.
  4. Casanova M, El-Baz A, Elnakib A, Switala A, Williams E, Williams D, Minshew N, Conturo T.  Quantitative analysis of the shape of the corpus callosum in patients with autism and comparison individuals.  Autism 2011 Mar;15(2):223-38. PMID: 21363871. PMCID: PMC3349188.
  5. Elnakib A, Casanova M, Gimel’farb G, Switala A, El-Baz A.  Autism diagnostics by 3D shape analysis of the corpus callosum.  Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. Suzuki K, ed. Hershey: IGI Global. 2012; pp. 315–35.

Early Detection of Dyslexia

  1. Nitzken M, Casanova M, Gimel’farb G, Inanc T, Zurada J, El-Baz A.  Shape analysis of human brain: a brief survey.  IEEE Journal of Biomedical and Health Informatics 2014 Jul;18(4):1337-54. PMID: 25014938.
  2. Elnakib A, Soliman A, Nitzken M, Casanova MF, Gimel’farb G, El-Baz A.  Magnetic resonance imaging findings for dyslexia: a review.  Journal of Biomedical Nanotechnology 2014 Oct;10(10):2778-805. PMID: 25992418.
  3. Elnakib A, Casanova M, Gimel’farb G, Switala A, El-Baz A.  Dyslexia diagnostics by 3D shape analysis of the corpus callosum.  IEEE Transactions on Information Technology in Biomedicine 2012 Jul;16(4):700-8. PMID: 22334032.
  4. Williams E, El-Baz A, Nitzken M, Switala A, Casanova M.  Spherical harmonic analysis of cortical complexity in autism and dyslexia.  Translational Neuroscience 2012 Mar;3(1):36-40. PMID: 22545198. PMCID: PMC3336871.

PubMed Information