Dr. Faranak Aghaei Profile

Dr. Faranak Aghaei

Algorithm Engineer

SPIE Involvement:

Author

Area of Expertise:

Computer Aided Diagnosis , Medical Image Processing , Tumor Segmentation , Graphical User Interface

Profile Summary

I am a Ph.D. Candidate in Electrical Engineering program at The University of Oklahoma. I received my M.S. in Electrical Engineering from the University of Oklahoma. I have been Research Assistant in Stephenson Reseach and Technology Center (SRTC) from Fall 2013 working in Computer Aided Diagnosis Lab. I have been doing research on Breast MRI, Brain MRI and CT images for different tasks. During my research, I had the chance to work on different projects with Mercy Hospital and University of Oklahoma Health Science Center (OUHSC).

Publications (19)

Proceedings Article | 18 March 2019 Presentation + Paper

Presentation + Paper

Computer aided antibody screening for IHC assay development

Faranak Aghaei, Yao Nie

Proceedings Volume 10956, 109560C (2019) https://doi.org/10.1117/12.2512613

KEYWORDS: Image resolution, Machine learning, Deconvolution, Feature extraction, Tissues, Data modeling, Pathology, Visualization, Neural networks, Computer aided design

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Proceedings Article | 15 March 2019 Presentation + Paper

Presentation + Paper

Design, fabrication and evaluation of non-imaging, label-free pre-screening tool using quantified bio-electrical tissue profile

Ali Zarafshani, Yunzhi Wang, Seyedeh-Nafiseh Mirniaharikandehei, Morteza Heidari, Faranak Aghaei, Siqi Wang, Liangzhong Xiang, Bin Zheng

Proceedings Volume 10953, 1095304 (2019) https://doi.org/10.1117/12.2513052

KEYWORDS: Tissues, Breast, Breast cancer, Mammography, Cancer, Computer aided design, Tissue optics, Machine learning, Feature extraction, Mathematics

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The design, fabrication, and performance of pre-screening tool using bioelectrical tissue profile is described. A probe of 8 electrodes using active-probe-sensing (AP-sensing) module was linearity distortion in the frequency response bonded to the 8 sub-regions of the breast surface forming the Bioimpedance transfer measurement system. Each measurement channel acquired the data from 1/8 of breast according to each breast defined in 8 sub-regions with split current injection channel and response behavioural bioelectricity detection. For this tissue bioelectrical measurement system, the electrodes were placed on the breast surface and before the bioelectrical profile were measured, we investigated a closed loop technique to compensate for the effects and measure the channel-skin-contact impedance. The AP-sensing module and connecting pads could be placed in the measurement electrode-Bras according to different breast size, and all measurement sub-regions should be equivalent for each case, but could be different in scale of 1- 2cm in related to different breast size. Bilateral structure was applied to compare each breast tissue bioelectrical properties in related to tissue behavioural in different frequency. Bioelectrical measurement efficiency was evaluated by the use of bioelectrical plots equivalent to a theoretical plot of the pure tissue profile versus average intracellular and extracellular and admittance behavioural of breast tissue able to flow electric field using Cole-Cole structure tissue modelling as a well-known bioelectrical tissue profile. The bioelectrical tissue profile as a pre-screening tool using theoretical pure tissue profiles and experimental measurements were evaluated in related a conventional Bioimpedance spectroscopy instruments. Breast bioelectrical profile of different breast density categories and average measurement values were significant, according to exist of fibro-glandular tissues in the total breast volume. The different of the theoretical values corresponding to pure fatty and fibro-glandular breast tissue behaviour was slightly different with the experimental measurements. We demonstrated the bioelectrical profile of breast tissue and extracting bioelectrical features that comparing in a bilateral structure to apply bioelectrical features as a supplementary data in the machine learning algorithms and present correspond risk factor for susceptibility of breast cancer in future studies. The preliminary equivalent theoretical and experimental results, evaluate the possibility of this new, non-imaging and label-free quantitative technique.

Proceedings Article | 15 March 2019 Presentation + Paper

Presentation + Paper

Developing a computer-aided image analysis and visualization tool to predict region-specific brain tissue “at risk” for developing acute ischemic stroke

Gopichandh Danala, Morteza Heidari, Faranak Aghaei, Bappaditya Ray, Bin Zheng

Proceedings Volume 10953, 109530M (2019) https://doi.org/10.1117/12.2512850

KEYWORDS: Brain, Image segmentation, Neuroimaging, Image analysis, Visualization, Artificial intelligence, Blood, Computer aided design, Blood circulation, Tissues

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Proceedings Article | 13 March 2019 Paper

Paper

Developing a new quantitative imaging marker to predict pathological complete response to neoadjuvant chemotherapy

Faranak Aghaei, Alan Hollingsworth, Seyedehnafiseh Mirniaharikandehei, Yunzhi Wang, Hong Liu, Bin Zheng

Proceedings Volume 10950, 109502O (2019) https://doi.org/10.1117/12.2512655

KEYWORDS: Breast, Magnetic resonance imaging, Image segmentation, Breast cancer, Cancer, Computer aided design, Computer aided diagnosis and therapy, Principal component analysis, Surgery

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Proceedings Article | 9 March 2018 Paper

Paper

Developing a unique portable device to non-invasively detect bio-electrochemical characteristics of human tissues

Ali Zarafshani, Sreeram Dhurjaty, Seyedeh-nafiseh Mirniaharikandehei, Faranak Aghaei, Liangzhong Xiang, Bin Zheng

Proceedings Volume 10573, 105735L (2018) https://doi.org/10.1117/12.2293507

KEYWORDS: Breast, Tissues, Breast cancer, Cancer, Electrodes, Bismuth, Calibration, Mammography, Skin, Signal detection

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Proceedings Article | 6 March 2018 Presentation + Paper

Presentation + Paper

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Alan Hollingsworth, Rebecca Stoug, Melanie Pearce, Hong Liu, Bin Zheng

Proceedings Volume 10579, 105790R (2018) https://doi.org/10.1117/12.2288001

KEYWORDS: Breast, Magnetic resonance imaging, Cancer, Breast cancer, Diagnostics, Computer aided diagnosis and therapy, Image enhancement, Image segmentation, Machine learning, Computer-aided diagnosis

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Proceedings Article | 27 February 2018 Paper

Paper

Association between mammogram density and background parenchymal enhancement of breast MRI

Faranak Aghaei, Gopichandh Danala, Yunzhi Wang, Ali Zarafshani, Wei Qian, Hong Liu, Bin Zheng

Proceedings Volume 10575, 105752O (2018) https://doi.org/10.1117/12.2293377

KEYWORDS: Breast, Magnetic resonance imaging, Mammography, Breast cancer, Cancer, Computer aided diagnosis and therapy, Image segmentation, Image enhancement, Machine learning

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Proceedings Article | 27 February 2018 Paper

Paper

Applying a new mammographic imaging marker to predict breast cancer risk

Faranak Aghaei, Gopichandh Danala, Alan Hollingsworth, Rebecca Stoug, Melanie Pearce, Hong Liu, Bin Zheng

Proceedings Volume 10575, 105753N (2018) https://doi.org/10.1117/12.2287556

KEYWORDS: Breast cancer, Mammography, Breast, Computer aided diagnosis and therapy, Cancer, Tumor growth modeling, Tissues, Chest, Image segmentation

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Proceedings Article | 27 February 2018 Paper

Paper

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

Seyedehnafiseh Mirniaharikandehei, Ali Zarafshani, Morteza Heidari, Yunzhi Wang, Faranak Aghaei, Bin Zheng

Proceedings Volume 10575, 105753F (2018) https://doi.org/10.1117/12.2291527

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Breast cancer, Cancer, Computer aided design, Image fusion, Breast, Machine learning, Performance modeling, Computer-aided diagnosis

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Proceedings Article | 27 February 2018 Paper

Paper

Computer-aided classification of breast masses using contrast-enhanced digital mammograms

Gopichandh Danala, Faranak Aghaei, Morteza Heidari, Teresa Wu, Bhavika Patel, Bin Zheng

Proceedings Volume 10575, 105752K (2018) https://doi.org/10.1117/12.2293136

KEYWORDS: Image segmentation, Breast, Mammography, Computer aided diagnosis and therapy, Computer aided design, Image classification, Tumors, Tissues, Digital mammography, Image acquisition, Image contrast enhancement

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Proceedings Article | 13 March 2017 Presentation + Paper

Presentation + Paper

Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

Seyedehnafiseh Mirniaharikandehei, Omkar Patil, Faranak Aghaei, Yunzhi Wang, Bin Zheng

Proceedings Volume 10138, 101380I (2017) https://doi.org/10.1117/12.2254163

KEYWORDS: Image segmentation, Ovarian cancer, Computed tomography, Cancer, Pancreatic cancer, Imaging informatics, Computer-aided diagnosis, Oncology, Medicine, Computer aided diagnosis and therapy, Signal attenuation, Tissues, Spine, Databases, Clinical trials

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Proceedings Article | 13 March 2017 Presentation + Paper

Presentation + Paper

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

Faranak Aghaei, Stephen Ross, Yunzhi Wang, Dee Wu, Benjamin Cornwell, Bappaditya Ray, Bin Zheng

Proceedings Volume 10138, 1013805 (2017) https://doi.org/10.1117/12.2254094

KEYWORDS: Brain, Image segmentation, Computed tomography, Blood, Computer-aided diagnosis, Computer aided diagnosis and therapy, Skull, Image processing algorithms and systems, Tissues, Image processing, Visualization

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Proceedings Article | 10 March 2017 Paper

Paper

Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Alan Hollingsworth, Yunzhi Wang, Yuchen Qiu, Hong Liu, Bin Zheng

Proceedings Volume 10136, 101361P (2017) https://doi.org/10.1117/12.2254073

KEYWORDS: Breast cancer, Tumor growth modeling, Image processing, Cancer, Data modeling, Performance modeling, Mammography, Process modeling, Computer aided diagnosis and therapy, Tissues, Image segmentation, Breast, Computer aided design, Image compression

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Proceedings Article | 10 March 2017 Paper

Paper

Developing a visual sensitive image features based CAD scheme to assist classification of mammographic masses

Yunzhi Wang, Faranak Aghaei, Maxine Tan, Yuchen Qiu, Hong Liu, Bin Zheng

Proceedings Volume 10136, 101361M (2017) https://doi.org/10.1117/12.2249499

KEYWORDS: Visualization, Computer aided diagnosis and therapy, Image visualization, Image segmentation, Image fusion, Computer aided design, Image classification, Mammography, Feature extraction, Image processing algorithms and systems

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Proceedings Article | 3 March 2017 Paper

Paper

Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

Kevin Gaffney, Faranak Aghaei, James Battiste, Bin Zheng

Proceedings Volume 10134, 101342I (2017) https://doi.org/10.1117/12.2254501

KEYWORDS: Brain, Image segmentation, Magnetic resonance imaging, Computer-aided diagnosis, Neuroimaging, Surgery, Image processing, Brain cancer, Radiotherapy, Tissues, Tumors, Computer aided diagnosis and therapy

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Proceedings Article | 24 March 2016 Paper

Paper

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

Maxine Tan, Faranak Aghaei, Yunzhi Wang, Wei Qian, Bin Zheng

Proceedings Volume 9785, 97851V (2016) https://doi.org/10.1117/12.2216313

KEYWORDS: Computer aided diagnosis and therapy, Computer-aided diagnosis, Breast, Mammography, Feature extraction, Breast cancer, Warning signs, Image segmentation, Digital mammography, Cancer, Computer aided design, Databases

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Proceedings Article | 24 March 2016 Paper

Paper

Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms

Maxine Tan, Faranak Aghaei, Alan Hollingsworth, Rebecca Stough, Hong Liu, Bin Zheng

Proceedings Volume 9785, 97850R (2016) https://doi.org/10.1117/12.2208246

KEYWORDS: Breast, Magnetic resonance imaging, Cancer, Computer aided diagnosis and therapy, Mammography, Breast cancer, Image analysis, Computer-aided diagnosis

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Proceedings Article | 24 March 2016 Paper

Paper

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

Faranak Aghaei, Maxine Tan, Alan Hollingsworth, Bin Zheng, Samuel Cheng

Proceedings Volume 9785, 978522 (2016) https://doi.org/10.1117/12.2216326

KEYWORDS: Breast, Image segmentation, Magnetic resonance imaging, Tumors, Image enhancement, Breast cancer, Image fusion, Chromium, Cancer, Computer aided diagnosis and therapy

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Proceedings Article | 20 March 2015 Paper

Paper

Automated detection of breast tumor in MRI and comparison of kinetic features for assessing tumor response to chemotherapy

Faranak Aghaei, Maxine Tan, Bin Zheng

Proceedings Volume 9414, 941429 (2015) https://doi.org/10.1117/12.2081616

KEYWORDS: Tumors, Breast, Magnetic resonance imaging, Image fusion, Image segmentation, Image enhancement, Image processing, Breast cancer, Computer aided diagnosis and therapy, Image analysis

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Showing 5 of 19 publications

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