faculty at Shahrekord University, Iran.
Dr. Maryam Taghizadeh is an Assistant Professor of Communication Engineering at Shahrekord University, Iran. With a Ph.D. from Isfahan University of Technology, her research focuses on signal and image processing, particularly in medical applications such as brain tumor segmentation and autism diagnosis using EEG and deep learning. She has published extensively in reputable journals and has actively contributed to academic conferences as a chair and committee member. Dr. Taghizadeh is also a dedicated mentor, guiding graduate research in biomedical signal processing and AI-driven diagnostics.
🎓Academic Background:
Dr. Maryam Taghizadeh holds a distinguished academic background in Electrical and Communication Engineering. She earned her Ph.D. in Communication Engineering from Isfahan University of Technology in 2014, following her M.Sc. (2003) and B.Sc. (2001) degrees from the same institution. Her educational journey laid a strong foundation in signal processing, communications, and applied research, setting the stage for her impactful contributions in both academia and biomedical research.
Profile:
💼 Professional Experience:
Dr. Maryam Taghizadeh has been serving as an Assistant Professor at the Faculty of Technology and Engineering, Shahrekord University, Iran, since 2014. In this role, she has actively engaged in teaching, research, and student supervision in the fields of communication engineering and biomedical signal processing. Her expertise lies in image analysis, EEG signal interpretation, and the application of artificial intelligence in medical diagnostics. She has led numerous research projects related to brain disorders such as autism and brain tumors, using advanced techniques like deep learning, wavelet transforms, and active contour models. In addition to her academic responsibilities, Dr. Taghizadeh has contributed significantly to the scientific community by participating in the organization of major conferences. She served as the Chairperson of the 3rd International Conference on Pattern Recognition and Image Analysis and has been a Scientific and Executive Committee Member in multiple editions of national and international conferences on machine vision and image processing. Through her research, mentorship, and leadership, she continues to play a vital role in advancing interdisciplinary engineering and healthcare innovation in Iran and beyond.
🧪 Scientific and Research Excellence:
Dr. Maryam Taghizadeh has demonstrated outstanding scientific and research excellence in the interdisciplinary fields of signal processing, image analysis, and biomedical engineering. Her pioneering work in developing automated methods for brain tumor segmentation, EEG-based autism diagnosis, and functional brain network analysis reflects a deep integration of engineering principles with healthcare innovation. She has published high-impact research in internationally recognized journals, such as IET Computer Vision and the International Journal of Neural Systems, showcasing both originality and practical relevance. Dr. Taghizadeh’s ability to apply machine learning, deep networks, and wavelet transforms to real-world medical problems marks her as a leader in AI-driven healthcare solutions. Furthermore, her active roles in organizing international conferences and mentoring graduate students further amplify her contribution to advancing science and fostering the next generation of researchers.
🧠 Areas of Expertise:
Dr. Maryam Taghizadeh’s research bridges the fields of signal and image processing, biomedical engineering, and artificial intelligence, with a special focus on medical applications. Her work includes developing algorithms for brain tumor segmentation, autism detection using EEG signals, and analyzing bioacoustic patterns in insects. She has authored and co-authored several impactful papers in reputable journals such as IET Computer Vision and International Journal of Neural Systems. Her research is widely recognized for its innovation in applying deep learning, wavelet transforms, and active contour models to real-world medical challenges.
👩🏫 Student Mentorship:
As a dedicated mentor, Dr. Maryam Taghizadeh has supervised numerous master’s theses in areas such as EEG signal processing, autism detection, cognitive brain analysis, and deep learning models for healthcare diagnostics. Her students have explored cutting-edge topics, contributing to the advancement of neuroscience and computational medicine under her guidance.
🌐 Academic Leadership:
Beyond her research, Dr. Maryam Taghizadeh has played a vital role in academic leadership. She has served on the Scientific Committee of the Iranian Conference on Machine Vision and Image Processing and the International Conference on Pattern Recognition and Image Analysis. Notably, she chaired the 3rd edition of the latter and was a member of the executive committee for its 6th edition in 2023, showing her ongoing engagement in shaping research dialogue and collaboration.
Publications:
-
A Review of Coronary Vessel Segmentation Algorithms
M.T. Dehkordi, S. Sadri, A. Doosthoseini
Journal of Medical Signals & Sensors, 1(1), pp. 49–54, 2011.
Cited by: 81
-
Local Feature Fitting Active Contour for Segmenting Vessels in Angiograms
M. Taghizadeh Dehkordi, A.M. Doost Hoseini, S. Sadri, H. Soltanianzadeh
IET Computer Vision, 8(3), pp. 161–170, 2014.
Cited by: 37
-
Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals
H.A. Ardakani, M. Taghizadeh, F. Shayegh
International Journal of Neural Systems, 32(11), Article 2250046, 2022.
Cited by: 17
-
Segmentation of Coronary Vessels by Combining the Detection of Centerlines and Active Contour Model
M. Taghizadeh, S. Sadri, A. Doosthoseini
Proceedings of the 7th Iranian Conference on Machine Vision and Image Processing, 2011.
Cited by: 8
-
A New Active Contour Model for Tumor Segmentation
M.T. Dehkordi
3rd International Conference on Pattern Recognition and Image Analysis, 2017.
Cited by: 6
-
An Effective System to Detect Face Drowsiness Status Using Local Features in a Hierarchical Decision-Making Structure
M.T. Dehkordi, F.S. Hafshejani, H. Pourghasem
International Research Journal of Engineering and Technology, 5(4), pp. 646–654, 2018.
Cited by: 3
-
Bioacoustics of Trogoderma granarium Everts (Coleoptera: Dermestidae)
P.A. Farsani, N.S. Dehkordi, R. Ebrahimi, A. Nemati, M.T. Dehkordi
Journal of Asia-Pacific Entomology, 27(1), Article 102189, 2024.
Cited by: 1
-
An Automated Method for Brain Tumor Segmentation Based on Level Set
Maryam Taghizadeh Dehkordi
International Journal of Computer, 30(1), pp. 59–69, 2018.
Cited by: 1
-
Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network
F. Bahrami, M. Taghizadeh, F. Shayegh
Journal of Medical Signals & Sensors, 14(8), Article 24, 2024.
Cited by: 0 (as of now)