Sharif University of Technology
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ABOUT

PERSONAL DETAILS
Azadi, Ave., Tehran, Tehran Province, Iran
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fatemizadeh@sharif.edu
+98 21 6616 4351
School of Electrical Engineering

BIO

ABOUT ME

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Project title

Project subtitle

Project description
.02

RESEARCH

Field of Interest:

Medical Image Processing, Analysis, and Understanding:

  • Medical Image Registration
  • Medical Image Segmentation

Computational Neurosciences:

  • fMRI Data Analysis
  • Human Brain Connectivity Analysis

Machine Learning:

  • Deep Learning
  • Statistical Pattern Recognition

Computer Vision and Digital Image Processing

.03

Courses

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SORTING BY DATE
23 Sep 2018

Deep Learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Graduate Course Selected

Deep Learning

Graduate Course Selected
About The Publication
Syllabus:
♦ Introductions: An Introduction to Machine Learning Concepts, importance, applications, and examples.
♦ Essential mathematics for machine learning: Linear Algebra and random variables.
♦ Shallow and Deep Neural networks as classifier and function approximator: Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), error back propagation (EBP) algorithm, most important theorems.
♦ Regularization, Normalization, and Optimization with emphasis on stochastic gradient descent (SGD) and its variations.
♦ Convolutional Neural Networks (CNN): History, foundations, architecture, learning.
♦ Application of CNN in computer vision: Most important network (AlexNet , GoogleNet , VGGNet , ResNet , and state of art architecture).
♦ Recurrent Neural Networks: RNN, LSTM, GRU, and state of art networks, learning and applications in natural language and signal processing.
♦ Unsupervised Learning: Auto Encoder (AE), Variational Auto Encoder (VAE), Conditional Variational Auto Encoder (CVAE)
♦ Adversarial learning: Generative Adversarial Networks (GAN) and Conditional GAN (CGAN), mathematical foundation, architecture, applications, and most important networks (GAN, DCGAN, CycleGAN , WGAN, and state of art)
♦ Diverse applications, current status and future of deep learning

References:
1) I. Goodfellow, etc., Deep Learning, MIT Press, 2016. 
2) S. Theodoridis, Machine Learning: A Bayesian and Optimization Approach, Academic Press, 2015
3) Top Hot Papers  
Lecture Videos (as taught 1400-1401-1):
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11 Feb 2005

Digital Image Processing

Graduate Course

Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased. [WiKi]
Graduate Course

Digital Image Processing

Graduate Course
About The Publication
Syllabus:
Image Acquisition and Simple Transformation
Image Sampling and Quantization
Two Dimensional System Theory
Image Enhancement in Spatial Domain
Image Enhancement in Frequency Domain
Image Restoration and Optimal Image Processing
Color Fundamentals and Color Image Processing
A Brief on Wavelets and its Application in Image Processing
Image Compression
Image Segmentation
Morphological Image
Processing Representation and Description
References:
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th Ed., 2018, Prentice Hall. 
A. K. Jain, Fundamental of Digital Image Processing, 1989, Prentice Hall. 
J. C. Russ, The Image Processing Handbook, 4th Edition, 2002, CRC Press.
Lecture Videos (as taught 1400-1401-1):
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11 Feb 2005

Medical Image Analysis and Processing (MIAP)

Medical Image analysis includes all the steps of processing, which are used for quantitative measurements as well as abstract interpretations of medical images. These steps require a-priori knowledge on the nature and content of the images, which must be integrated into the algorithms on a high level of abstraction. Thus, the process of image analysis is very specific, and developed algorithms can rarely be transferred directly into other domains of applications.
Graduate Course

Medical Image Analysis and Processing (MIAP)

Graduate Course
About The Publication
Syllabus:
A Review on Digital Image Processing
Advanced Methods in Medical Image Noise Removal
Non Local Mean (NLM)
Nonlinear Anisotropic Diffusion Filtering
Total Variation
Wavelet Denoising
Sparse Image Denoising
Advanced Methods in Medical Image Segmentation:
Statistical Methods (GMM, PNN, MLP, …)
Region Based
Parametric and Geometric Deformable Models
Medical Image Registration:
Feature Based
Voxel Based
Medical Image Interpolation
References:
Principles and Advanced Methods in Medical Imaging and Image Analysis, A. P. Dhawan, H.K. Huang, and D. SH. Kim, 2008.
Biomedical Image Processing, Thomas M. Deserno (Editor), Springer-Verlag, 2011.
Medical Image Processing-Techniques and Applications, G. Dougherty, Springer-Verlag, 2011.
Advanced Biomedical Image Analysis, M. A. Haidekker, Wiley, 2011.
Biomedical Images Analysis, R. M. Rangayyan, 2005.
Handbook of Biomedical Image Analysis (3 Volumes), J. S. Suri, D. L. Wilson, and S. Laxaminarayan, 2005.
Mathematical Models for Registration and Applications to Medical Imaging, O. Scherzer, 2006.
Medical Image Analysis Methods, L. Costaridou, 2005.
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, By: T. S. Yoo, 2004.
Medical Image Processing, Reconstruction and Restoration: Concepts and Methods, J. Jan, 2005.
2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, A. A. Goshtasby, 2005.
Medical Image Registration, J. Hanjal, D. Hawkes, and D. Hill, 2001.
Handbook of Medical Imaging – Processing and Analysis, I. N. Bankman, 2000
Pattern Recognition for Medical Imaging, A. Meyer-Base, 2004.
Image Processing Techniques for Tumor Detection, M. Dekker.
Top survey papers.
Lecture Videos (as taught 1400-1401-1):
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01 Jan 1970

Statistical Pattern Recognition (SPR)

Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. These activities can be viewed as two facets of the same field of application, and they have undergone substantial development over the past few decades.
Graduate Course

Statistical Pattern Recognition (SPR)

Graduate Course
About The Publication
Syllabus:
Introduction to basics concepts and definition
Bayesian Decision Theory and Discrimination Functions
Linear Classifier
Nonlinear Classifier
Introduction to Deep Learning
Feature Selection and Feature Reduction
Feature Generation I, Basic
Feature Generation II, Application
Clustering and Validation
References:
S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th Ed., 2008, Academic Press. 
Ch. Bishop, Pattern Recognition and Machine Learning, 1st ed., 2006, Springer Verlage. 
R. Duda, P. Hart, and D. Stock, Pattern Classification, 2000, Wiley.
Lecture Videos (as taught 1400-1401-1):
[simple_icon name="googledrive"] Link #1 (Not Ready Yet)
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23 Sep 2004

Electric Circuit Theory

Circuit theory is fundamental to the study of important engineering applications such as power systems and communication. As such, it forms part of the first course to engineering and science students in several disciplines. The learning of circuit theory requires calculus, linear algebra, transform analysis, programming, and laboratory practice. These tools are also required for other courses and in professional careers later.
Undergraduate

Electric Circuit Theory

Undergraduate
About The Publication
Syllabus:
♦ Mutual Inductance and Transformers,
♦ Three-Phase Circuits (Balanced and Unbalanced),
♦ Graph Theory and Network Equations, and Tellegen Theorem,
♦ The Laplace Transform in Circuit Analysis,
♦ Natural Frequency,
♦ Transfer Function, Poles, and Zeros,
♦ Two-Port Circuits,
♦ Nodal and Mesh Analysis,
♦ Cut-Set and Loop Analysis,
♦ Modified Nodal Analysis.,
♦ State Equations.

References:
1) Ch. A. Desor and E. S. Kuh, Basic Circuit Theory, 1966, McGraw-Hill. 
2) C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 6th, McGraw Hill.
3) R. C. Dorf and J. A. Svoboda, Introduction to Electric Circuits, 9th, 2013, John Wiley.
4) J. W. Nilsson and S. Riedel, Electric Circuits, 11th, 2019, Pearson.

Lecture Videos (as taught 1400-1401-1):
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23 Sep 2005

Electric Circuits and Lab

Circuit analysis is the process of finding the voltages across, and the currents through, all circuit components. There are many techniques for calculating these values. However, for the most part, the techniques assume linear components. Except where stated, the methods described in this course are applicable only to linear network analysis.
Undergraduate

Electric Circuits and Lab

Undergraduate
About The Publication
Syllabus:
♦ Introduction to Circuits,
♦ Basic Concept and Famous Waveform,
♦ Voltage and Current Laws,
♦ Basic Components (R, L, C, VS, IS)
♦ Simple Circuits (Series, Parallels)
♦ Resistive Network Analysis (Thevenin and Norton Equivalent, Nodal and Mesh Analysis)
♦ First Order Circuits (RC and RL),
♦ Second Order Circuits (RLC, RC2, and RL2),
♦ Operational Amplifier (Op-Amp),
♦ Linear Circuits Analysis in Time Domain and Convolution Theorem,
♦ Sinusoidal Steady-State Analysis,
♦ Transfer Functions and Filters,
♦ AC Circuit Power Analysis.

References:
1) Ch. A. Desor and E. S. Kuh, Basic Circuit Theory, 1966, McGraw-Hill. 
2) C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 6th, McGraw Hill.
3) R. C. Dorf and J. A. Svoboda, Introduction to Electric Circuits, 9th, 2013, John Wiley.
4) J. W. Nilsson and S. Riedel, Electric Circuits, 11th, 2019, Pearson.

Lecture Videos (as taught 1400-1401-1):
[simple_icon name="googledrive"] Link #1
[simple_icon name="mediafire"] Link #2
22 Feb 2012

Signal and Systems

Signal processing plays an extremely important and continually growing role in a wide variety of engineering systems. Furthermore, technology and algorithms for signal processing continue to develop rapidly. While only a short time ago signal processing systems were predominantly analog, integrated circuit technology has made digital signal processing often preferable and more cost-effective.
Undergraduate

Signal and Systems

Undergraduate
About The Publication
Syllabus:
♦ Introduction,
♦ Signals and Systems,
♦ Linear Time-Invariant Systems and Convolution
♦ Fourier Series Representation of Periodic Signals,
♦ The Continuous-Time Fourier Transform,
♦ The Discrete-Time Fourier Transform,
♦ Time and Frequency Characterization of Signals and Systems,
♦ Sampling,
♦ The Laplace Transform,
♦ The Z-Transform.
References:
A. V. Oppenheim, A. S. Willsky, with S. H. Nawab, Signals and Systems, 2nd Ed., 1996, Pearson. 
S. Haykin and ,B. V. Veen, Signals and Systems, 2nd Ed., 2002, John Wiley.
.04

Publications

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Journal Papers

  1. I. Mousavian, M. B. Shamsollahi, and E.Fatemizadeh, “Noninvasive Fetal ECG Extraction Using Doubly Constrained Block-Term Decomposition,” Mathematical Biosciences and Engineering, vol. 17, no. 1, pp. 144-159, Sep. 2019. [doi]
  2. Sh. Ghahari, E.Fatemizadeh, and A. M. Nasrabadietc., “Studying the Distinction between Emotions in fMRI Data by Using Temporal Network Theory,” Frontiers in Biomedical Technologies, vol. 6, no. 2, pp. 87-93, Jun. 2019. [Link]
  3. N. Farahani, E.Fatemizadeh, and A. M. Nasrabadietc., “Using rDCM Method in the Mixed Model in Order to Inference Effective Connectivity in Emotions,” Frontiers in Biomedical Technologies, vol. 6, no. 2, pp. 106-113, Jun. 2019. [Link]
  4. Z. Turani, E.Fatemizadeh, and etc., “Optical Radiomic Signatures Derived from Optical Coherence Tomography Images Improve Identification of Melanoma,” Cancer Research, vol. 79, no. 8, pp. 2021-2030, April 2019. [doi]
  5. Z. Turani, E.Fatemizadeh, and etc., “Refractive Index Correction in Optical Coherence Tomography Images of Multilayer Tissues,” Journal of Biomedical Optics, vol. 23, no. 7, pp. 070501-(1-4), July 2018. [doi]
  6. N. Salehi, A. Rahmim, E. Fatemizadeh, and etc., “Cardiac Contraction Motion Compensation in Gated Myocardial Perfusion SPECT: A Comparative Study,” Physica Medica, vol. 49, pp. 77-82, May 2018. [doi]
  7. A. Shahbabaie, M. Ebrahimpoor, A. Hariri, M. A. Nitsche, J. Hatami, E. Fatemizadeh, and etc., “Transcranial DC Stimulation Modifies Functional Connectivity of Large-Scale Brain Networks in Abstinent Methamphetamine Users,” Brain and Behavior, vol. 8. no. 3, pp. 1-13, Feb. 2018. [doi]
  8. A. Fotoohinasab, E. Fatemizadeh, H.Pezeshk, and M. Sadeghi, “Denoising of genetic switches based on Parrondo’s paradox,” Physica A, vol. 493, pp. 410-420, Mar. 2018. [doi]
  9. A. Najafi, S. Janghorbani, S. A. Motahari, E. Fatemizadeh, “Statistical Association Mapping of Population-Structured Genetic Data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 2, pp. 638-649, March-April 2019. [doi]
  10. N. Sahba, E. Fatemizadeh, H. Behnam, “A Robust Dual Source Level Set Method for Three-Dimensional Echocardiography Image Segmentation,” Journal of Theoretical and Applied Information Technology, vol. 95. no. 21, Nov. 2017. [Link]
  11. M. Afzali, E. Fatemizadeh, and H.Solatanian-Zadeh, “Sparse registration of diffusion weighted images,” Computer Methods and Programs in Biomedicine, vol. 151, pp. 33-43, Nov. 2017. [doi]
  12. A. Ghaffari and E. Fatemizadeh, “Image Registration based on Low Rank Matrix: Rank-Regularized SSD,” IEEE Trans. on Medical Imaging, Vol. 39, no. 1, pp. 138-150, Aug. 2017. [doi]
  13. M. R. Mohammadi, E. Fatemizadeh, and M.H. Mahoor, “A joint dictionary learning and regression model for intensity estimation of facial AUs, ” Journal of Visual Communication and Image Representation, vol. 47, pp. 1–9, Aug. 2017. [doi]
  14. S. Adabi, Z. Turani, E. Fatemizadeh, A. Clayton, and M. R. Nasiriavanaki, “Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review,” Biomedical Engineering and Computational Biology, vol. 2017, no. 8, pp. 1-5, 2017, [doi]
  15. M. R. Mohammadi, E. Fatemizadeh, M. H. Mahoor, “An Adaptive Bayesian Source Separation Method for Intensity Estimation of Facial AUs,” IEEE Trans. on Affective Computing, vol. PP, no. 99, pp. 1-1 (Early Access), 2017, [doi]
  16. F. ZohourParvaz, E. Fatemizadeh, and H. Behnam, ” Speed Improvement in Graph-Cuts Based Registration for Non-Rigid Image Registration of Brain Magnetic Resonance Images,” Journal Signal and Data Processing, vol. 13, no. 4, pp. 79-92, Mar. 2017. [doi]
  17. N. Sahba, E. Fatemizadeh, and H. Behnam, “An optimized probabilistic edge based level set method for left ventricle segmentation in echocardiography images,” Biomedical Research, vol. 29, no. 8, pp. 3788-3793, [Link]
  18. M. Afzali, A. Ghaffari, E. Fatemizadeh, and H.Solatanian-Zadeh, “Medical image registration using sparse coding of image patches,” Computers in Biology and Medicine, vol. 73, pp. 56-70, June 2016. [doi]
  19. S. M. A. Beheshti, H. A. Noubari, E. Fatemizadeh, and M. Khalili, “Classification of abnormalities in mammograms by new asymmetric fractal features,” Biocybernetics and Biomedical Engineering, vol. 36, no. 1, PP. 56-65, 2016. [doi]
  20. A. Najafi, A. Joudaki, and E. Fatemizadeh, “Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping,” Pattern Analysis and Machine Intelligence, IEEE Tran. on, vol. 38, no. 7, pp. 1452-1464, Jul. 2016. [doi]
  21. A. Afzali, E. Fatemizadeh, and H. Solatanianzadeh, “Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model,” Journal of Neuroscience Methods, vol. 253, pp. 28-37, Sep. 2014. [doi]
  22. A. Ghaffari and E. Fatemizadeh, “RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure,” Image Processing, IEEE Tran. on, vol. 24, no. 12, pp. 5567 – 5580, Oct. 2015. [doi]
  23. N. Alamdari, S. Akbari, and E. Fatemizadeh, “Unsupervised versus Supervised Methods for Categorizing Mental States from fmri Data,” Journal of Medical Devices, vol. 9, no. 2, pp. 020949-1 – 020949-2, Jun. 2015. [doi]
  24. M. R. Mohammadi, E. Fatemizadeh, and M. H. Mahoor, “Intensity Estimation of Spontaneous Facial Action Units Based on Their Sparsity Properties,” Cybernetics, IEEE Tran. on, vol. 46, no. 3, pp. 817 – 826, April. 2015. [doi]
  25. A. Ghaffari and E. Fatemizadeh, “Robust Huber similarity measure for image registration in the presence of spatially-varying intensity distortion,” Signal Processing , vol. 109, pp. 54–68, April 2015. [doi]
  26. N. Salehi, E. Fatemizadeh, and et., “Cardiac Contraction Motion Correction in Gated Myocardial Perfusion SPECT Projection Domain,” Frontiers in Biomedical Technologies, vol. 2, no. 4, pp. 206-213, Dec. 2015. [Link]
  27. N. Amini, E. Fatemizadeh, and H. Behnam, “MRI and PET Image Fusion by Using Curvelet Transform,” Journal of Advances in Computer Research, vol. 5, no. 4, pp. 23–30, Nov. 2014. Link
  28. S. M. A. Beheshti, H. AhmadiNoubarin, E. Fatemizadeh, and M. Khalili, “An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms,” Journal of Digital Imaging, vol. 27, no. 5, pp. 661–669, Oct. 2014. [doi]
  29. A. Afzali, E. Fatemizadeh, and H. Solatanianzadeh, “High Angular Resolution Diffusion Image Registration,” Journal of Machine Vision and Image Processing, vol. 2, no. 1, pp. 1-9, Spring 2015. [Link]
  30. M. R. Mohammadi, E. Fatemizadeh, and M. H. Mahoor, “Non-negative sparse decomposition based on constrained smoothed ℓ0 norm,” Signal Processing , vol. 100, pp. 42–50, July 2014. [doi]
  31. M. R. Mohammadi, E. Fatemizadeh, and M. H. Mahoor, “PCA-based dictionary building for accurate facial expression recognition via sparse representation,” Journal of Visual Communication and Image Representation, vol. 25, no. 5, pp. 1082–1092, July 2014. [doi]
  32. A. Ghaffari and E. Fatemizadeh, “Sparse-induced similarity measure: mono-modal image registration via sparse-induced similarity measure,” IET Image Processing, 2014. [doi]
  33. N. Amini, E. Fatemizadeh, and H. Behnam, “MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules,” Journal of Medical Engineering & Technology, vol. 38, no. 4, pp. 211–219, May. 2014. [doi]
  34. Z. Ghassabi, J. Shanbehzadeh, A. Sedaghat, E. Fatemizadeh, “An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors,” EURASIP Journal on Image and Video Processing, vol. 25, Apr. 2013. ِ[doi]
  35. M. Tarjoman, E. Fatemizadeh, and K. Badie, “A Framework for Content-Based Human Brain Magnetic Resonance Images Retrieval Using Saliency Map,” Biomedical Engineering: Applications, Basis and Communications, vol. 25, pp. 1350045.1-1350045.9, Aug. 2013. [doi]
  36. M. M. Khalilzadeh, E. Fatemizadeh, and H. Behnam, “Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation,” Magnetic Resonance Imaging, vol. 31, no. 5, pp. 733-741, Jun. 2013. [doi]
  37. M. Tarjoman, E. Fatemizadeh, and K. Badie, “A Content-Based Approach to Medical Images Retrieval,” International Journal of Healthcare Information Systems and Informatics (IJHISI), vol. 8, pp. 15-27, Apr.-Jun. 2013. [doi]
  38. M. Afzali and E. Fatemizadeh, and H. Soltanian-Zadeh, “Interpolation of Orientation Distribution in High Angular Resolution Diffusion Magnetic Resonance Imaging,” Iranian J. Biomedical Engineering, vol. 7, no. 1, pp. 57-64, Spring 2013.
  39. M. Tarjoman, E. Fatemizadeh, and K. Badie, “An implementation of a CBIR system based on SVM learning scheme,” Journal of Medical Engineering & Technology, vol. 37, no. 1, pp. 43–47, Jan. 2013. [doi]
  40. M. M. Khalilzadeh, E. Fatemizadeh, and H. Behnam, “Adaptive Sparse Representation for MRI Noise Removal ” Biomedical Engineering: Applications, Basis and Communications, vol. 24, pp. 1-12, Sep. 2012. [doi]
  41. N. Gholizadeh, S. Akhlaghpoor, H. Soltanian-Zadeh, E. Fatemizadeh, M. R. Alinaghizadeh, A. S. Akhtari, et al., “Optimization of Liver Iron Load Assessment by Pixel-Based T2* MRI in Thalassemic Patients,” Open J. Radiology, vol. 2, pp. 46-5, Jun. 2012. [doi]
  42. E. Fatemizadeh and M. Maneshi, “A New Watermarking Algorithm based On Human Visual System for Content Integrity Verification of Region of Interest,” Computing and Informatics,, vol. 31, pp. 877-899, Jul. 2012. [doi]
  43. M. Tarjoman, E. Fatemizadeh, and K. Badie, “An Interactive CBIR System Based on ANFIS Learning Scheme for Human Brain Magnetic Resonance Images Retrieval,” Biomedical Engineering: Applications, Basis and Communications, vol. 24, pp. 27-36, Feb. 2012. [doi]
  44. M. Begol, K. Maghooli, and E. Fatemizadeh, “Fingerprint Liveness Detection based on Contourlet,” Science Series Data Report, vol. 4, pp. 102-106, Apr. 2012.
  45. E. Rezazadeh-Ardabili, K. Maghooli, and E. Fatemizadeh, “Contourlet Features Extraction and AdaBoost Classification for Palmprint Verification,” J. American Science, vol. 7, pp. 353-362, Jul. 2011.
  46. M. Rasooli, S. Ghofrani, and E. Fatemizadeh, “Farsi License Plate Detection based on Element Analysis and Characters Recognition,” Int. J. Signal Processing, Image Processing and Pattern Recognition, vol. 4, pp. 65-80, Mar. 2011.
  47. L. Bahreini, E. Fatemizadeh, and M. Guity, “Diagnostic Efficacy of All Series of Dynamic Contrast Enhanced Breast MR Images Using Gradient Vector Flow (GVF) Segmentation and Novel Border Feature Extraction for Differentiation Between Malignant and Benign Breast Lesions,” Iranian J. Radiology, vol. 7, pp. 225-234, 2010. [sid]
  48. A. Taalimi and E. Fatemizadeh, “A New Mathematical Approach for Detection of Active Area in Human Brain fMRI Using Nonlinear Model,” Biomedical Engineering: Applications, Basis and Communications, vol. 22, pp. 409–418, Oct. 2010. [doi]
  49. M. Rafi-Nazari and E. Fatemizadeh, “A CBIR System for Human Brain Magnetic Resonance Image Indexing,” Int. J. Computer Applications, vol. 7, no. 14, pp. 33-37, Oct. 2010.
  50. A. Besharat and E. Fatemizadeh, “Using Genetic Algorithm for Optimization of Mammograms Image Compression,” Majlesi J. Electrical Engineering, vol. 4, pp. 7-18, Sep. 2010. [sid]
  51. E. Fatemizadeh and P. Shooshtari, “ROI-Based 3D Human Brain Magnetic Resonance Images Compression Using Adaptive Mesh Design and Region-Based Discrete Wavelet Transform,” Int. J. Wavelets, Multiresolution and Information Processing, vol. 8, pp. 407-430, 2010. [doi]
  52. A. Taalimi and E. Fatemizadeh, “Activation Detection in fMRI Images using Nonlinear Models: Wiener-Hammerstein and NARMA,” Iranian J. Biomedical Engineering, vol. 4, pp. 231-248, Fall 2010. [sid]
  53. Yousefi-Rizi, A. Ahmadian, E. Fatemizadeh, J. Alirezaie, and N. Rezaie, “Intrathoracic Airway Tree Segmentation from CT Images Using a Fuzzy Connectivity Method,” Iranian J. Medical Physics, vol. 6, pp. 71-83, Spring 2009. [sid]
  54. M. Torabi and E. Fatemizadeh, “Alzheimer’s Disease Diagnosis using Nonlinear Weighted T1-MRI Classification,” Iranian J. Biomedical Engineering, vol. 3, pp. 213-226, Fall 2009. [sid]
  55. F. Yousefi-Rizi, A. Ahmadian, J. Alirezaie, E. Fatemizadeh, and N. Rezaie, “A Robust to Leakage Method for Airway Tree Segmentation Based on Shape Feature Optimization,” Iranian J. Biomedical Engineering, vol. 2, pp. 165-178, Fall 2008.
  56. E. Fatemizadeh and P. Shooshtari, “A Selective Three Dimensional Magnetic Resonance Images Compression Method Using Adaptive Mesh Design and Region-Based Wavelet Transform,” Iranian J. Biomedical Engineering, vol. 2, pp. 191-202, Fall 2008.
  57. E. Fatemizadeh, C. Lucas, and H. Soltanian-Zadeh, “Automatic landmark extraction from image data using modified growing neural gas network,” Information Technology in Biomedicine, IEEE Transactions on, vol.7, no.2, pp.77-85, June 2003. [doi]
  58. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. Hashemi-Golpayegani, “Sensitivity Analysis of Landmarks in Medical Image Registration,” Shaahed Daaneshvar Journal, vol. 9, pp. 33-46, Mar. 2002. [sid]
  59. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. Hashemi-Golpayegani, “Local Warping of Medical Images Using Voronoi Images,” Modarres J. Medical Sciences, vol. 5, pp. 101-113, Jun. 2002. [sid]

Conference Papers

  1. Z. Turani, E. Fatemizadeh, and etc, “Optical Radiomic Signatures Derived from OCT Images to Improve Identification of Melanoma,” Proc. SPIE 11078, Optical Coherence Imaging Techniques and Imaging in Scattering Media III, 110780O, pp. 31-34, 23-27 Jun. 2019. [doi]
  2. Z. Turani, E. Fatemizadeh, and etc, “Compensation of Refractive Index Variation in Optical Coherence Tomography Images ,” Proc. SPIE 10867, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIII, 108672W, pp. 123-129, 2-7 Feb. 2019. [doi]
  3. S. Haghparast and E. Fatemizadeh, “A Novel Frame Rate Up-Conversion Method to Decrease Blocking and Blurring Factors using Non-Rigid Image Registration on Medical Videos ,” Biomedical Engineering (ICBME), 2018, 22th Iranian and 3rd International Conference of, pp. 1-4, 29-30 Nov. 2018. [doi]
  4. Sh. Akbari, and E. Fatemizadeh, “Effects of Non-Linear Correlation Measures on Brain Functional Connectivity in Parkinson’s Disease,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 762-765, 18-21 July, 2018. [doi]
  5. Sh. Akbari, E. Fatemizadeh and R. Deevband, “Functional Brain Networks in Parkinson’s Disease,” 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), pp. 330-333, 30 Nov.-1 Dec., 2017. [doi]
  6. Z. Turani, E. Fatemizadeh and M. Nasiri Avanaki, “Hierarchical Enhancement of Optical Coherence Tomography Images,” 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), pp. 1-4, 30 Nov.-1 Dec., 2017. [doi]
  7. M. Haghir Ebrahim-Abadi and E. Fatemizadeh, “A Clustering-Based Algorithm for De Novo Motif Discovery in DNA Sequences,” 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), pp. 1-6, 30 Nov.-1 Dec., 2017. [doi]
  8. M. Afzali, S. H. Sardouie, E. Fatemizadeh and H. Soltanian-Zadeh, “Canonical polyadic decomposition for principal diffusion direction extraction in diffusion weighted imaging,” 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, pp. 122-127, 2-4 May, 2017. [doi]
  9. Z. Turani, E. Fatemizadeh, and etc., “Noise reduction in OCT skin images,” Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013724, pp. 552-557, 13 March 2017. [doi]
  10. M. Almasganj, S. Adabi, E. Fatemizadeh, and etc., “A spatially-variant deconvolution method based on total variation for optical coherence tomography images,” Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013725, pp. 558-563, 13 March 2017. [doi]
  11. Z. Turani, E. Fatemizadeh, and etc., “Refractive Index Correction in Optical Coherence Tomography Images,” The 23rd Iranian Conference on Optics and Photonics (ICOP 2017) and the 9th Iranian Conference on Photonics Engineering and Technology (ICPET 2017), pp. 1133-1136, Jan. 31- Feb. 2, 2017. [Link]
  12. V. Khodadadi, E. Fatemizadeh, and S. K. Setarehdan, “Optimized kalman filter based on second momentum and triple rectangular for cell tracking on sequential microscopic images ,” Biomedical Engineering (ICBME), 2015 22th Iranian Conference of, pp. 251-256, 25-27 Nov. 2015. [doi]
  13. Khodadadi, E. Fatemizadeh, and S. K. Setarehdan, “Overlapped cells separation algorithm based on morphological system using distance minimums in microscopic images ,” Biomedical Engineering (ICBME), 2015 22th Iranian Conference of, pp. 263-268, 25-27 Nov. 2015. [doi]
  14. M. S. Majdi and E. Fatemizadeh, “ Laplacian mixture model point based registration,” Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, pp. 57-60, 10-12 Nov. 2015. [doi]
  15. H. M. Kalkhoran and E. Fatemizadeh, “Pain level estimation in video sequences of face using incorporation of statistical features of frames,” Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, pp. 172-175, 10-12 Nov. 2015. [doi]
  16. H. Akbari, H. M. Kalkhoran, and E. Fatemizadeh, “A robust FCM algorithm for image segmentation based on spatial information and Total Variation,” Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, pp. 180-184, 10-12 Nov. 2015. [doi]
  17. F. Taheri-Dezaki, A. Ghaffari, E. Fatemizadeh, “GMWASC: Graph matching with weighted affine and sparse constraints,” Computer Science and Software Engineering (CSSE), 2015 International Symposium on, pp. 1-5, 18-19 Aug. 2015. [doi]
  18. A Hariri, S. Arabshahi, A. Ghafari, and E. Fatemizadeh, “Medical images stabilization using sparse-induced similarity measure,” Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on, pp. 1-4, March 2015. [doi]
  19. F. Ghaffari-YousefZadeh, E. Fatemizadeh, and M. Pouladian, “Developing, Implementation, and Evaluation of a MRI Simulator Using DWT ” in the 23th Iranian Conference on Electrical Engineering (ICEE’15), May 2014, pp 832-837.
  20. M. Ghalehnovi, E. Zahedi, and E. Fatemizadeh, “Integration of spatial fuzzy clustering with level set for segmentation of 2-D angiogram,” Biomedical Engineering and Sciences (IECBES), 2014 IEEE EMBS Conference on, pp. 309-314, 8-10 Dec. 2014. [doi]
  21. A. Alizadeh, E. Fatemizadeh, and M. R. Deevband, “Investigation of Brain Default Network’s activation in autism spectrum disorders using Group Independent Component Analysis ,” Biomedical Engineering (ICBME), 2014 21th Iranian Conference of, pp. 177-180, 26-28 Nov. 2014. [doi]
  22. H. Aliniazare, H. Behnam, and E. Fatemizadeh, “Echocardiography frames quantification by empirical mode decomposition method,” Biomedical Engineering (ICBME), 2014 21th Iranian Conference of, pp. 201-205, 26-28 Nov. 2014. [doi]
  23. M. Farzi, A. Ghaffari, and E. Fatemizadeh, “Online undersampled dynamic MRI reconstruction using mutual information,” Biomedical Engineering (ICBME), 2014 21th Iranian Conference of, pp. 241-245, 26-28 Nov. 2014. [doi]
  24. M. Afzali, E. Fatemizadeh, and H. Soltanian-Zadeh, “Sparse representation-based super-resolution for diffusion weighted images,” Biomedical Engineering (ICBME), 2014 21th Iranian Conference of, pp. 12-16, 26-28 Nov. 2014. [doi]
  25. M. F. Azampour, A. Ghaffari, A. Hamidi-Nekoo, and E. Fatemizadeh, “Manifold Learning Based Registration Algorithms Applied to Multimodal Images,” Engineering in Medicine and Biology Society, 2014. EMBS 2014. 36th Annual International Conference of the IEEE, pp. 1030-1034, 26-30Aug. 2014. [doi]
  26. Z. Assari, H. Behnam, and E. Fatemizadeh, and Z. Alizadeh-Sani “Automatic Extraction of Cardiac Key Cycles/Frames from Sequence of  Echocardiographic Images, Starting Arbitrary Frame, based on Manifold Learning” in The 22th Iranian Conference on Electrical Engineering (ICEE’14), May 2014, pp 2575-2580.
  27. M. R. Mohammadi, E. Fatemizadeh, and M. H. Mahoor, “Simultaneous recognition of facial expression and identity via sparse representation,” Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, pp. 1066,1073, 24-26 March 2014. [doi]
  28. S. Khoubani, H. Sh. Nadjar, E. Fatemizadeh, and E. Mohammadi, “A two layer texture modeling based on curvelet transform and spiculated lesion filters for recognizing architectural distortion in mammograms,” Biomedical Engineering (MECBME), 2014 Middle East Conference on, pp. 21-24, 17-20 Feb. 2014. [doi]
  29. S. M. A. Beheshti, H. A. Noubari, E. Fatemizadeh, M. Rezaee, and M. Khalili, “Mammograms enhancement using wavelet transform and piecewise linear and nonlinear coefficient mapping,” Biomedical Engineering (MECBME), 2014 Middle East Conference on , pp. 107-110, 17-20 Feb. 2014. [doi]
  30. S. Tavakoli and E Fatemizadeh, ” Decoding the Long Term Memory Using Weighted Thresholding Union Subspaces Based Classification on Magnetoencephalogram,” Artificial Intelligence and Signal Processing, International Symposium, AISP’13, pp. 164-171, Dec. 2013. [doi]
  31. A. H. Nekoo and E. Fatemizadeh, “Nonrigid Registration of Breast MR Images Using Intensity-Unbiased Force in Variational Motion Estimation,” 1st IEEE EMBS International Students Conference (EMBS ICS’13), pp. ??-??, 8-10 Oct. 2013. Link. Best Paper Awarded.
  32. A. Ghaffari and E. Fatemizadeh, “Mono-modal image registration via corr-entropy measure,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 223-226, 10-12 Sep. 2013. [doi]
  33. M. Afzali, E. Fatemizadeh, and H. Soltanian-Zadeh, “High angular resolution diffusion image registration,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 232-236, 10-12 Sep. 2013. [doi]
  34. A. H. Nekoo, A. Ghaffari, and E. Fatemizadeh, “Nonrigid registration of breast MR images using residual complexity similarity measure,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 241-244, 10-12 Sep. 2013. [doi]
  35. A. Ghaffari and E. Fatemizadeh, “Sparse based similarity measure for mono-modal image registration,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 462-466, 10-12 Sep. 2013. [doi]
  36. A. Oliaee, A. Ghaffari, and E. Fatemizadeh, “MRI image reconstruction via new K-space sampling scheme based on separable transform,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 127-130, 10-12 Sep. 2013. [doi]
  37. N. T. Alamdari and E. Fatemizadeh, “Comparison of classification and dimensionality reduction methods used in fMRI decoding,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 175-179, 10-12 Sep. 2013. [doi]
  38. E. Mohammadi, E. Fatemizadeh, H. Sh. Nadjar, H.; Khoubani, S., “A textural approach for recognizing architectural distortion in mammograms,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 136-140, 10-12 Sep. 2013. [doi]
  39. M. R. Mohammadi and E. Fatemizadeh, “Fuzzy local binary patterns: A comparison between Min-Max and Dot-Sum operators in the application of facial expression recognition,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 315-319, 10-12 Sep. 2013. [doi]
  40. S. Khoubani, E. Fatemizadeh, H. Sh. Nadjar, and E. Mohammadi, “A two layer texture modeling based on curvelet transform and Spectral multi-fractal curvelet  for recognizing architectural distortion in mammograms,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 158-163, 10-12 Sep. 2013.
  41. M. R. Mohammadi and E. Fatemizadeh, and M. H. Mahoor, “Constrained Smoothed ℓ0 norm for Fast Sparse non-negative Solution ,” Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, pp. 386-390, 10-12 Sep. 2013.
  42. M. R. Mohammadi, E. Fatemizadeh, and M. Mahoor, “Robust Face Recognition in Various Facial Expression Using of  Sparse Representation ” in The 21st Iranian Conference on Electrical Engineering (ICEE’13), pp. 1-6, May 2013.
  43. A. Ghaffari, E. Fatemizadeh, and I. Esmaeili, “Face Recognition Using Combination of  Sparse Representation and Nearest Sub-Space Classifiers,” in The First Iranian Conference on Pattern Recognition and Image Analysis (PRIA’13), pp ??-??, Mar. 2013.
  44. M. Afzali, E. Fatemizadeh, and H. Soltanian-Zadeh, “Interpolation of orientation distribution functions (ODFs) in Q-ball imaging,” Biomedical Engineering (ICBME), 2012 19th Iranian Conference of, pp. 213-217, 20-21 Dec. 2012. [doi]
  45. A. Ghaffari, R. Khorsandi, and E. Fatemizadeh, “Landmark and intensity based image registration using free form deformation,” Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, pp. 768-771, 17-19 Dec. 2012. [doi]
  46. A. Ghanbari, R. Abbasi-Asl, A. Ghaffari, and E. Fatemizadeh, “Automatic B-spline image registration using histogram-based landmark extraction,” Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, pp. 1004-1008, 17-19 Dec. 2012. [doi]
  47. N. S. M. Ehteshami, M. Tabandeh, and E. Fatemizadeh, “A new ROI extraction method for FKP images using global intensity,” Telecommunications (IST), 2012 Sixth International Symposium on,  pp. 1147-1150, 6-8 Nov. 2012. [doi]
  48. R. Fouladi, E. Fatemizadeh, and Sh. Arab, “Inference of Gene Regulatory Networks by Extended Kalman Filtering using Gene Expression Time Series Data,” in International Conference on Bioinformatics Models, Methods and Algorithm (BIOINFORMATICS’12), 2012, pp. 150-155. [Link]
  49. H. Karimi and E. Fatemizadeh, “Segmentation of Iris off-Angle Images,” in The 18th Iranian Conference of Biomedical Engineering (ICBME’11), pp. 540-545, Dec. 2011.
  50. M. Afzali, E. Fatemizadeh, and H. Soltanian-Zadeh, “Effect of Different Diffusion Maps on Registration Results,” Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1-5, 16-17 Nov. 2011. [doi]
  51. R. Abbasi-Asl and E. Fatemizadeh, “MMRO: A Feature Selection Criterion for MR Images Based on Alpha Stable Filter Responses,” Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1-5, 16-17 Nov. 2011. [doi]
  52. S. Jahani, S. K. Setarehdan, and E. Fatemizadeh, “Automatic Identification of Overlapping/Touching Chromosomes in Microscopic Images Using Morphological Operators,” Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1-4, 16-17 Nov. 2011. [doi]
  53. E. Atlasi Roshani and E. Fatemizadeh, “Sensitivity Analysis of Landmarks and its Effect on Performance of Image Registration Methods,” Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1-4, 16-17 Nov. 2011.
  54. M. E. Abbasi-Dezfouli, S. Arab, E. Fatemizadeh, and N. Hosseynimanesh, “Finding protein active sites using approximate sub-graph isomorphism,” Biomedical Engineering (MECBME), 2011 1st Middle East Conference on, pp. 432-435, 21-24 Feb. 2011. [doi]
  55. F. Siyahjani, A. Ghaffari, and E. Fatemizadeh, “Mammogram image retrieval via sparse representation,” Biomedical Engineering (MECBME), 2011 1st Middle East Conference on, pp. 63-66, 21-24 Feb. 2011. [doi]
  56. F. Siyahjani and E. Fatemizadeh, “Content based mammogram image retrieval based on the multiclass visual problem,” Biomedical Engineering (ICBME), 2010 17th Iranian Conference of, pp. 1-4, 3-4 Nov. 2010. [doi]
  57. L. Bahreini, E. Fatemizadeh, and M. Gity, “Gradient vector flow snake segmentation of breast lesions in Dynamic Contrast-Enhanced MR images,” Biomedical Engineering (ICBME), 2010 17th Iranian Conference of, pp. 1-4, 3-4 Nov. 2010. [doi]
  58. R. Khorsandi, E. Fatemizadeh, and A. Heidari, “Nonrigid Multi-Modal Brain Image Registration Using Mutual Information, B-Spline Function – Sensitivity Analysis,” Biomedical Engineering (ICBME), 2010 17th Iranian Conference of, pp. 1-4, 3-4 Nov. 2010.
  59. M. Ramezani, A. Heidari, E. Fatemizadeh, and H. Soltanian-Zadeh, “Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain,” Intelligent and Advanced Systems (ICIAS), 2010 International Conference on, pp. 1-5, 15-17 June 2010. [doi]
  60. M. Akhbari, M. Babaie-Zadeh, E. Fatemizadeh, and C. Jutten, “An entropy based method for activation detection of functional MRI data using Independent Component Analysis,” Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pp. 2014-2017, 14-19 March 2010. [doi]
  61. M. Ramezani and E. Fatemizadeh, “Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data, ” in Data mining in Biomedicine (DMINBIO’09), 2009, pp. 75-83. [doi]
  62. M. Akhbari and E. Fatemizadeh, “Application of independent component analysis for activation detection in functional magnetic resonance imaging (FMRI) data,” Statistical Signal Processing, 2009. SSP ’09. IEEE/SP 15th Workshop on, pp. 129-132, Aug. 31 2009-Sept. 3 2009. [doi]
  63. H. Davoudi, A. Taalimi, and E. Fatemizadeh, “Extracting activated regions of fMRI data using unsupervised learning,” Neural Networks, 2009. IJCNN 2009. International Joint Conference on, pp. 641-645, 14-19 June 2009. [doi]
  64. A. Taalimi, H. Bayati, and E. Fatemizadeh, “Clustering method for fMRI activation detection using optimal number of clusters,” Neural Engineering, 2009. NER ’09. 4th International IEEE/EMBS Conference on, pp. 171-174, April 29 2009-May 2 2009. [doi]
  65. M. Dadgostar, P. R. Tabrizi, E. Fatemizadeh, and H. Soltanian-Zadeh, “Feature Extraction Using Gabor-Filter and Recursive Fisher Linear Discriminant with Application in Fingerprint Identification,” Advances in Pattern Recognition, 2009. ICAPR ’09. Seventh International Conference on, pp. 217-220, 4-6 Feb. 2009. [doi]
  66. S. Ghaedi-Oskooei, M. Dadgostar, G. Rezai Rad, and E. Fatemizadeh, “Adaptive watermarking scheme based on ICA and RDWT,” Crime Detection and Prevention (ICDP 2009), 3rd International Conference on, pp. 1-4, 3-3 Dec. 2009. [doi]
  67. M. Ramezani and E. Fatemizadeh, “Detecting Functional Connectivity in the Resting Brain using Independent Component Analysis,” in 16th Iranian Conference on Biomedical Engineering (ICBME’09), Dec. 2009.
  68. M. Akhbari and E. Fatemizadeh, “Estimating Components of Functional Magnetic Resonance Imaging (fMRI) Data in a Time-Dependent order by Modifying ICA Algorithms,” in The 16th Iranian Conference on Biomedical Engineering (ICBME’09), Dec. 2009.
  69.  A. Tavakol, H. Soltanian-Zadeh, S. Akhlaghpour, and E. Fatemizadeh, “Malignancy Determination of Tumors Using Perfusion MRI,” in International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV ’09), Las Vegas Nevada, USA, 13-16 Jul. 2009, pp. 905-906. [Link]
  70. A. Taalimi and E. Fatemizadeh, “”fMRI activation detection by obtaining BOLD response of extracted balloon parameters with Particle Swarm Optimization,” EUROCON 2009, EUROCON ’09. IEEE, pp. 1437-1442, 18-23 May 2009. [doi]
  71. M. SiyahMansoory, H. Behnam, and E. Fatemizadeh, “Landmark Extraction from Echocardiography Sequence Based on Corner Detection Algorithms Using Product Moment Coefficient Matcher,” in 2009 International Conference on Signal Processing Systems, May 2009, pp. 91-97.
  72. M. SiyahMansoory, H. Behnam, and E. Fatemizadeh, “Automatic Cardiac Motion Quantification by Extracting Landmarks from an Echocardiography Sequence ,” in The 8th International Conference on Applications of Electrical Engineering (AEE ’09) , pp. 159-165, May 2009.
  73. M. SiyahMansoory, H. Behnam, and E. Fatemizadeh, “Landmark Extraction from Echocardiography Sequence Based on Corner Detection Algorithms Using Gradient Vector Matcher,” in International Association of Computer Science and Information Technology – Spring Conference (IACSITSC’09), April 2009, pp. 510-516.
  74. R. Shoja-Ghiass and E. Fatemizadeh, “Illumination and View Invariant Face Detection and Recognition in Images with Complex Background,” Visual Media Production (CVMP 2008), 5th European Conference on, pp. 1-9, 26-27 Nov. 2008. [Link].
  75. R. Shoja-Ghiass and E. Fatemizadeh, “Multi-view face detection and recognition under varying illumination conditions by designing an illumination effect cancelling filter,” Signal Processing Algorithms, Architectures, Arrangements, and Applications (SPA), 2008, pp. 27-32, 25-27 Sept. 2008. [Link].
  76. R. Shoja-Ghiass, E. Fatemizadeh, and F. Marvasti, “Designing an illumination effect canceling filter in facial images for multi-view face detection and recognition in images with complex background,” Telecommunications, 2008. IST 2008. International Symposium on, pp. 809-814, 27-28 Aug. 2008. [doi]
  77. H. Bayati, H. Davoudi, and E. Fatemizadeh, “A heuristic method for finding the optimal number of clusters with application in medical data,” Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 4684-4687, 20-25 Aug. 2008. [doi]
  78. F. Yousefi Rizi, A. Ahmadian, E. Fatemizadeh, and J. Alirezaie, “An optimization based approach embedded in a fuzzy connectivity algorithm for airway tree segmentation,” Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 4011-4014, 20-25 Aug. 2008. [doi]
  79. M. Dadgostar, P. Roshani Tabrizi, H. Soltanian-Zadeh, and E. Fatemizadeh, “Fingerprint Recognition Based on Gabor Filter and FLD/RFLD Transformation,” in The 5th Iranian Conference on Machine Vision and Image Processing (MVIP’08), Aug. 2008.
  80. M. Dadgostar, E. Fatemizadeh, and M. B. Shamsollahi, “Fingerprint Recognition Using Gabor Filter and a New Mosaicking Method,” in The 5th Iranian Conference on Machine Vision and Image Processing (MVIP’08), Aug. 2008, pp. 432-437.
  81. M. Khalilzadeh and E. Fatemizadeh, “fMRI Time-Series Classification Using Neural-Gas and Hard C-Means Clustering,” in The 5th Iranian Conference on Machine Vision and Image Processing (MVIP’08), Aug. 2008.
  82. F. Yousefi Rizi, A. Ahmadian, N. Sahba, V. Tavakoli, J. Alirezaie, E. Fatemizadeh, et al., “A Hybrid Fuzzy Based Algorithm for 3D Human Airway Segmentation,” Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on, pp. 2295-2298, 16-18 May 2008. [doi]
  83. R. Shoja-Ghiass, N. Sadati, and E. Fatemizadeh, “A New Method for Multi-View and Illumination Variation Face Recognition Using Support Vector Machine,” in The 16th Iranian Conference on Electric Engineering (ICEE’08), May 2008, pp. 262-267.
  84. A. Ansaar, E. Fatemizadeh, and H. Behnam, “Registration of Digital Subtraction Angiography Using Wavelet Transform and Multi-Resolution Processing,” in The 14th Iranian Conference on Biomedical Engineering (ICBME’08), Feb. 2008, pp. 1-10.
  85. M. Torabi and E. Fatemizadeh, “Nonlinear Discrimination of Gray and White Matter Weighted Images for Hierarchial Alzheimer Disease Diagnosis,” in The 14th Iranian Conference on Biomedical Engineering (ICBME’08), Feb. 2008, pp. 108-115.
  86. M. Torabi, H. Moradzadeh, R. Vaziri, S. Razavian, R. D. Ardekani, M. Rahmandoust, et al., “Development of Alzheimer’s Disease Recognition using Semiautomatic Analysis of Statistical Parameters based on Frequency Characteristics of Medical Images,” Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on, pp. 868-871, 24-27 Nov. 2007. [doi]
  87. M. H. Kadbi, E. Fatemizadeh, A. Eslami, and M. Khosroshahi, “A New Method for Colourizing of Multichannel Mr Images Based on Real Colour of Human Brain,” in 15th European Signal Processing Conference (EUSIPCO 2007), Sep. 2007, pp. 449-453.
  88. E. Behnamghader, R. D. Ardekani, and E. Fatemizadeh, “Another Approach to Detection of Abnormalities in MR-Images Using Support Vector Machines,” Image and Signal Processing and Analysis, 2007. ISPA 2007. 5th International Symposium on, pp. 98-101, 27-29 Sep. 2007. [doi]
  89. S. M. E. Sahraeian and E. Fatemizadeh, “Wavelet-Based 2-D ECG Data Compression Method Using SPIHT and VQ Coding,” EUROCON, 2007. The International Conference on “Computer as a Tool”, pp. 133-137, 9-12 Sep. 2007. [doi]
  90. N. Mohammadiha, B. V. Vahdat, and E. Fatemizadeh, “A Machine Vision Measurement of Steel Villet Camber During Molding Process,” in The Ninth International Conference on Pattern Recognition and Information Processing (PRIP’2007), May 2007.
  91. M. Torabi, H. Moradzadeh, R. Vaziri, R. D. Ardekani, and E. Fatemizadeh, “Multiple Sclerosis Diagnosis Based on Analysis of Subbands of 2-D Wavelet Transform Applied on MR-images,” Computer Systems and Applications, 2007. AICCSA ’07. IEEE/ACS International Conference on, pp. 717-721, 13-16 May 2007. [doi]
  92. M. Dadgostar, E. Fatemizadeh, and M. B. Shamsollahi, “Detection of Reference Point in Fingerprint Using Combination of Complex Filters and Directional Images,” in The 15th Iranian Conference of Electrical Engineering (ICEE’07), May 2007, pp. 25-29.
  93. N. Mohammadi and E. Fatemizadeh, “MS Lesion Extraction from Magnetic Resonance Images,” in The 15th Iranian Conference of Electrical Engineering (ICEE’07), May 2007, pp. 25-29.
  94. O. Sayyadi and E. Fatemizadeh, “A Fast Algorithm for Enhancing Digital Cephalic Radiography Using Mixture Models and Local Gamma Correction based on the Gamma Map Contours,” in The 13th Iranian Conference on Biomedical Engineering (ICBME’07), Feb. 2007.
  95. G. Mohammadi and E. Fatemizadeh, “A New 2D Landmark Extraction Approach Based on Covariance of Intensity Variation in a Mask ,” in The 13th Iranian Conference on Biomedical Engineering (ICBME’07), Feb. 2007.
  96. G. Mohammadi and E. Fatemizadeh, “A New 2D Corner Detector for Extracting Landmarks from Brain MR Images ,” in International Symposium on Signal Processing and its Application(ISSPA2007), Jan. 2007.
  97. S. Parvaneh, E. Fatemizadeh, and H. Behnam, “Optimizing Entropy-based Thresholding Speed Using Particle Swarm Optimization (PSO),” in The 4th Iranian Conference of Machine Vision and Image Processing (MVIP’07), Feb. 2007.
  98. R. D. Ardekani, M. Torabi, and E. Fatemizadeh, “Breast Cancer Diagnosis and Classification in MR-images using Multi-stage classifier,” Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on, pp. 84-87, 11-14 Dec. 2006. [Link].
  99. M. Torabi, R. D. Ardekani, and E. Fatemizadeh, “Discrimination between alzheimer’s disease and control group in MR-images based on texture analysis using artificial neural network,” Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on, pp. 79-83, 11-14 Dec. 2006. [Link].
  100. S. Parvaneh, E. Fatemizadeh, and H. Behnam, “MS Lesion Detection Using Hybrid Method,” in Cairo International Biomedical Engineering Conference (CIBEC’06), Dec. 2006, pp. 1-3.
  101. L. Amini, H. Soltanian-Zadeh, E. Fatemizadeh, and G. Hosseinzadeh, “Automatic Landmark Generation for Nonlinear Registration of Anatomical and Functional Brain MRI ” in Cairo International Biomedical Engineering Conference (CIBEC’06), 2006, pp. 1-3.
  102. M. Eslami and E. Fatemizadeh, “Edge Sensitive Block Motion Estimation Employing Partial Ridgelet Distortion Search,” Signal Processing and Information Technology, 2006 IEEE International Symposium on, pp. 902-907, Aug. 2006. [doi]
  103. A. Emadi and E. Fatemizadeh, “An Adaptive Method for Steganography Using Wavelet Transform,” in The 14th Iranian Conference on Electrical Engineering (ICEE’06), 2006, pp. 1-6.
  104. S. Parvaneh, E. Fatemizadeh, and H. Behnam, “MS Lesion Detection Using a Novel Approach Combined with Entropy Based Thresholding.,” in The 14th Iranian Conference on Electrical Engineering (ICEE’06), 2006, pp. 1-6.
  105. A. Azimi and E. Fatemizadeh, “The Iterative Closest Point based on thin Plate Spline in Monomodal Image Registration,” The 4th International Conference on Computer Science and its Applications (ICCSA02006), Jun. 2006.
  106. E. Nezhadarya, M. B. Shamsollahi, and E. Fatemizadeh, “MRI Denoising Using Sureshrink in Contourlet Space,” in The 12th International Conference on Biomedical Engineering (ICBME 2005), Singapore, Dec. 2005.
  107. J. Hashemi and E. Fatemizadeh, “Biometric Identification through Hand Geometry,” Computer as a Tool, 2005. EUROCON 2005.The International Conference on, pp. 1011-1014, 21-24 Nov. 2005. [doi]
  108. N. Naadvar and E. Fatemizadeh, “Patients Private Data Hiding Within ECG Signals,” in The 12 th Iranian Conference on Biomedical Engineering (ICBME’05), Oct. 2005, pp. 256-264.
  109. J. Hashemi and E. Fatemizadeh, “Human Authentication Using Hand Geometry,” in The 12 th Iranian Conference on Biomedical Engineering (ICBME’05), Oct. 2005, pp. 120-125.
  110. P. J. M. Fard, M. Vafadoust, S. Parvaneh, and E. Fatemizadeh, “A New Method for Eye Movement Tracking,” in The 12 th Iranian Conference on Biomedical Engineering (ICBME’05), Oct. 2005, pp. 326-333.
  111. P. Mortazavian, M. Jahangiri, and E. Fatemizadeh, “A Combinational Technics for Medical Imges Watermarking and Steganography,” in The Third Conference on Machine Vision, Image Processing and Applications (MVIP’05), Tehran-Iran, Feb. 2005, pp. 415-421.
  112. S. Shobeihi and E. Fatemizadeh, “Medical Image Enhancement using Modified MultiScale Retinex Algorithm,” in The Third Conference on Machine Vision, Image Processing and Application (MVIP’05), Feb. 2005, pp. 400-407.
  113. P. Mortazavian, M. Jahangiri, and E. Fatemizadeh, “A Low Degradation Steganography Model For Data Hiding In Medical Images,” in Fourht IASTED International Conference Visualization, Imaging, and Image Processing, 2004, pp. 914-920.
  114. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. H. Golpayegani, “Local Warping of Medical Images Using Elliptic Basis Function,” in The 7th Annual Computer Society of Iran Computer Conference, 2002, pp. 300-308.
  115. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. H. Golpayegani, “Multiple Precision Landmark Extraction for Medical Images with Growing Cellular Neural Network,” in The 7th Annual Computer Society of Iran Computer Conference, 2002, pp. 309-317.
  116. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. H. Golpayegani, “A New Method for Local Warping of Magnetic Resonance Images Using Voronoi Image,” in The 9th Iranian Conference on Biomedical Engineering (ICBME’00), 2000, pp. 510-514.
  117. E. Fatemizadeh, C. Lucas, H. Soltanian-Zadeh, and M. R. H. Golpayegani, “Automatic Landmark Extraction from MRI using Growing Neural Gas Model,” in The 7th Iranian Conference on Electrical Engineering (ICEE’99), 1999, pp. 151-158.
  118. E. Fatemizadeh and C. Lucas. (1999, 11 Nov. 1999). Local Warping of Human Brain MR Images Using MLP Neural Networks. One Day Seminar on Medical Image processing.
  119. E. Fatemizadeh and C. Lucas. (1999, 30 Nov. – 4 Dec. 1999). Automatic Landmarks Extraction for Non-Ordered Points. 2nd Irano-Armenian Workshop on Neural Networks.
  120. E. Fatemizadeh and C. Lucas, “Priority Setting for Feature Vector Components with Genetic Algorithms,” in International Conference on Intelligent and Cognitive Systems, 1997, pp. 98-102.
  121. E. Fatemizadeh, M. R. H. Golpayegani, and M. Zardoshti, “Velocity Profile Modeling of Human Writing Using DCT Transform,” presented at the Third Electronics Conference, 1996.
.05

Students

Graduate Students

  1. A. Mohammad-Pazandeh, “ Deep Facial Expression Recognition,” PhD.
  2. M. F. Azampour, “Multimodal Medical Image Registration using Deep Learning,” PhD.
  3. M. Almasganj, “ A Robust Method Based on Level Set for Segmentation of Retinal OCT Images,” PhD.
  4. Z. Asgharian, "Functional Connectivity in Depressive Disorder Using Functional Magnetic Resonance Imaging Data in Auditory Stimulation Mode,", MSc.
  5. K. Ansarino, "Registration of MRI-CT Images of Human Brain with Deep Learning, ", MSc.

Alumni

  1. F. JalilPiran, “ Dynamic Functional Connectivity in Autism Spectrum Disorder Using Resting-State fMRI,” MSc, 2022.
  2. M. J. Rezaee, “ Modeling of “Arousal/Valence”-“Facial Expression” with Age/Gender Consideration,” MSc, 2020.
  3. Z. Turani, “Light-Tissue Interaction Modeling and Optical Properties Extraction Using Optical Coherence Tomography Images in order to Detection of Malignant Melanoma,” PhD, 2020.
  4. N. Hosseini-Naghavi, “ Brain Connectivity Analysis Using Multiple Partial Least Square with fMRI Signals,” MSc, 2020.
  5. S. Haghparast, “Echocardiography Image Rate UpConversion using Registration,” MSc, 2019.
  6. A. Moaven, “ A Novel Structural Based Similarity Measure for MRI and Ultrasound Registration,” MSc, 2018.
  7. M. H. Eybpoosh, “Predicting Patient Clinical Data Using Radiomic Features,” MSc, 2018.
  8. M. Haghir-EbrahimPour, “Motif Finding in DNA Sequences by Using Machine Learning Approach,” MSc, 2018.
  9. A. Mohammad-Pazandeh, “Human Action Recognition in Smart Houses,” MSc, 2016
  10. M. Almasganj, “A Deconvolution Method Based on 3D PSF Estimation for SS-OCT Image Quality Enhancement,” MSc, 2016.
  11. M. Afzali-Deligani, “Registration of High Angular Resolution Diffusion Images,” PhD, 2016.
  12. F. Taheri, “Image Registration using Graph-Based Methods,” MSc, 2015.
  13. H. Mohebbi, “Pain Level Estimation Using Facial Expression,” MSc, 2015.
  14. A. Hariri, “Model-Based Analyzing of tDCS on Brain Networks of MethamPhetamine Addicts,” MSc, 2015.
  15. M. S. Majdi, “Developing Robust Image Similarity Measure in Feature Based Image Registration,” MSc, 2015.
  16. M. R. Mohammadi, “Facial Action Units Detection via Sparse Representation,” PhD, 2015.
  17. A. Ghaffari, “Robust Similarity Measure in Ultrasound Image Registration,” PhD, 2015.
  18. A. Najafi, “Genome-Wide Association Study via Machine Learning Techniques,” MSc, 2014
  19. M. Farzi, “MRI Reconstruction Using Partial k-space Scans ,” MSc, 2014.
  20. M. F. Azampour, “Image Matching Using Manifold Learning Based Algorithms,” MSc, 2014.
  21. A. Akbarian-Aghdam, “Functional Connectivity in fMRI Resting-State Baseline Network in High Functioning Autism Disorder,” MSc, 2014.
  22. S. Tavakoli  “Decoding the Long-Term Memory Magnetoencephalogram,” MSc, 2014.
  23. A. Hamidi-Nekoo “Elastic Registration of Breast Magnetic Resonance Images” MSc, 2013.
  24. R. Abbasi-Asl, “Feature-Based Local Registration of Brain MR Image,” MSc, 2012.
  25. A. Oliaee, “Fast Magnetic Resonance Image Reconstruction Using Compressed Sensing,” MSc, 2012.
  26. Sh. Gharachorlu, “Anatomical Surface Modeling via Harmonic Fields,” MSc, 2012.
  27. D. Ebrahimi, “Design a Content-Based Color Image Retrieval Using Attention Driven Saliency Map,” MSc, 2012.
  28. R. Khorsandi, “Non-Rigid Medical Image Registration Based on Information Theory,” MSc, 2011.
  29. R. Fouladi, “Bayesian Filtering Approach to Improve Gene Regulatory Networks Modeling via Gene Expression Time Series,” MSc, 2011.
  30. M. E. Abbasi-Dezfouli, “Protein Function Prediction Using Protein Structure and Computational Methods,” MSc, 2011.
  31. F. Siyahjani, “Mammography Computer-Aided Diagnosis Based on Content-Based Image Retrieval Using Visual Features,” MSc, 2010.
  32. H. Salimi, “Implementation of Real-Time Object Recognition on DM6467 Digital Video Evaluation Module,” MSc 2010.
  33. M. Ramezani, “Functional Connectivity Detection in Resting-State Brain using functional Magnetic Resonance Imaging,” MSc, 2010.
  34. M. Akhbari, “Blind Source Separation Analysis of Brain fMRI for Activation Detection,” MSc, 2010.
  35. A. Taalimi, “Activation Detection in fMRI using Nonlinear Time Series Analysis,” MSc, 2009.
  36. R. Shoja-Ghiass, “Design and Implementation of a Multi-View Face Recognition System,” MSc, 2009.
  37. M. Rafi-Nazari, “CBIR System for Human Brain Magnetic Resonance Image Indexing,” MSc 2009.
  38. N. Babapour, “Protein Function Prediction using Protein Interaction Networks,” MSc, 2009.
  39. M. Gazorpak, “Watermarking of a Fingerprint Image,” MSc 2009.
  40. R. Dehestani-Ardakani, “Gene Regulation Prediction using Data Mining Techniques,” MSc, 2008.
  41. M. Maneshi, “ROI-based Watermarking for Images Verfication in Telemedicine,” MSc, 2007.
  42. P. Shooshtari, “Compression of Magnetic Resonance Images Using Properties of Region of Interest,” MSc, 2006.
  43. G. Mohammadi, “Three Dimensional Landmarks Extraction from Human Brain Magnetic Resonance Images,” MSc, 2006.
  44. M. H. Kadbi, “Pseudo Coloring of Magnetic Resonance Images of Human Brain,” MSc, 2006.
  45. A. Emadi, “A Robust Steganography Method for Medical Images,” MSc, 2006.
  46. A. Azimi, “Registration of Magnetic Resonance Images Using a Modified Iterative Closest Point Algorithm,” MSc, 2006.
  47. J. Hashemi, “Design of an Authentication System Based on Hand Gesture Biometric,” MSc, 2005.
.06

RESUME

EDUCATION
  • 1995
    2003
    TEHRAN, IRAN

    ELECTRICAL ENGINEERING - PHD

    Tehran University

    Thesis Title: Human Brain Magnetic Resonance Image Registration Using Intelligent Methods

    Supervisor: Dr. C. Lucas

    Co-Supervisor: Dr. H. SoltanianZadeh

  • 1992
    1995
    TEHRAN, IRAN

    BIOMEDICAL ENGINEERING - MSC

    AMIRKABIR UNIVERSITY OF TECHNOLOGY

    Thesis Title: Parkinson Disease Treatment Evaluation Using Writing Modelling

    Supervisor: Dr. M. R. Hashemi-Golpayegani

  • 1986
    1991
    TEHRAN, IRAN

    ELECTRICAL ENGINEERING - BSC

    Sharif University of Technology

    Thesis Title: Human Bladder Volume Change Detection Using Electrical Impedance Measurement

    Supervisor: Dr. A. AkbarZadeh

ACADEMIC AND EXECUTIVE POSITIONS
  • 2004
    2022

    Associate Professor

    Sharif University of Technology

  • 2018
    2021

    Vice-President for Student Affairs

    Sharif University of Technology

  • 2014
    2018

    Vice-Chairman for Academic Affairs

    Department of Electrical Engineering - Sharif University of Technology

  • 2013
    2014

    Vice-Chairman for Students Affairs

    Department of Electrical Engineering - Sharif University of Technology

  • 2014
    2018

    Head of Biomedical Group

    Department of Electrical Engineering - Sharif University of Technology

.07

CONTACT

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دانشجویان گرامی، فهرست زیر لیست مقدماتی و پردازش نشده، دانشجویان فاقد شاخه تحصیلی (دانشجویان ورودی 99 و تعدادی از دانشجویان ورودی 97 و 98) را در بر دارد

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