AminKatouzian

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Amin Katouzian

Amin8.jpg Dr. Amin Katouzian
Senior Research Scientist

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München
Room: MI 03.13.053

Phone: +49 89 289 17081
Fax: +49 89 289 17059

E-Mail:
Skype ID: Amin.Katouzian

Education

  • Ph.D. in Biomedical Engineering, Columbia University, New York, USA
  • M.Sc. in Electrical Engineering, Fairleigh Dickinson University, New Jersey, USA
  • B.Sc. in Electrical Engineering, Sistan & Baluchestan University, IRAN

Awards

  • Samsung GRO Award of $100,000 per year up to three years.
  • Volcano Corporation (Rancho Cordova, CA) Fellowship, $78,100, Sep. 2008-Sep. 2010.
  • Siemens (Princeton, NJ) Fellowship, $50,000, Jun. 2007-Dec. 2007.
  • Boston Scientific (Fremont, CA) Fellowship, $122,500, Jan. 2006-Jun. 2007.
  • Colonel Fairleigh S. Dickinson (Teaneck, NJ) Scholarship, $7,500, Jan. 2002-Sep. 2003.
  • Third place in North New Jersey IEEE student paper presentation contest, graduate category, Teaneck, NJ, March 2003.

Research Interests

  • Computer Aided Surgery and Diagnosis.
  • Augmented Reality in Medical Procedures.
  • Computer Vision for Medical Applications.
  • Telemedicine.
  • Multidimensional Cue Extraction and Pattern Recognition.
  • Reconstruction, Registration, Visualization, and Segmentation of 2-4D Images.
  • Multiresolution Analysis.
  • Speech Recognition and Perception.

Active Research Projects

Estimation of Flow Parameters from X-ray Data

Estimation of Flow Parameters from X-ray Data

This project focuses on the quantification of blood flow based on 2D and 3D angiographic X-ray image data, which is of particular interest during minimally invasive interventional procedures such as recanalizations of stenotic vessels and embolizations of arteries that feed tumors or vascular malformations in the patient's brain, for example. Our projects cover the evaluation of model-based approaches involving CFD (computational fluid dynamics) simulations as well as methods that aim at estimating the optical flow in the images and deriving quantitative blood flow information from these results.
Big Data Analysis for Medical Applications

Big Data Analysis for Medical Applications

Widespread use of electronic health records (EHR) led to vast amount of medical data being collected. The data is characterized by large sample size, heterogeneity of variables, unstructured information, missing information, and time-dependent variables, to name a few. These facts render commonly used statistical tools to perform analysis inadequate and require the development of sophisticated algorithms to overcome these challenges. For instance, the set of variables recorded for each patient can naturally be decomposed into groups, known as views. Most machine learning algorithms ignore this multi-view relationship. Instead, they can either be trained on each view separately or on a concatenation of all views to form a single view. Considering these relationships recently came to attention of the research community that proposed co-training, multiple kernel learning and subspace learning to address this problem. Although existing methods show promising results in their respective tasks, it is difficult to apply them to real-world clinical data, where highly heterogeneous features (continuous, categorial, ordinal) and missing values are common, because not all tests can be performed on all patients. As a result, multi-view learning algorithms have rarely been applied to medical problems.
Knowledge Propagation Models for Image Registration

Knowledge Propagation Models for Image Registration

To register modalities with complex intensity relationships, we leverage machine learning algorithm to cast it into a mono modal registration problem. This is done by extracting tissue specific features for propagating anatomical/structural knowledge from one modalitiy to an other through an online learnt propagation model. The registration and propagation steps are iteratively performed and refined. For proof-of-concept, we employ it for registering (1) Immunofluorescence to Histology images and (2) Intravascular Ultrasound to Histology Images.
OCT Tissue Classification

OCT Tissue Classification

Optical coherence tomography (OCT), employing light rather than ultrasound, is a high-resolution imaging technology that permits a precise assessment of biological tissue. Used intravascular, OCT is increasingly used for assessing safety and efficacy of intracoronary devices, such as drug-eluting stents and bioabsorbable stents. Obtained images provide insights regarding stent malposition, overlap, and neointimal thickening, among others. Recent OCT histopathology correlation studies have shown that OCT can be used to identify plaque composition, and hence it is possible to distinguish “normal” from “abnormal” neointimal tissue based on its visual appearance. The aim of this project is to develop a novel method for the automatic analysis of tissue in IVOCT. Automatic tissue classification will allow for a quantitative and potentially more time-efficient and objective analysis of IVOCT data. For instance, classifying neointimal tissue as either “mature” or “immature” can be used to assess the disease state of patients; as a potential predictor of late stent-failure events such as stent thrombosis and restenosis.
Ultrasound-Based Brainshift Estimation and Correction

Ultrasound-Based Brainshift Estimation and Correction

In the context of intraoperative navigation for neurosurgery, brainshift still represents one of the main challenges. After skull and dura mater opening, brain tissue undergoes dynamic spatial modification due to gravity, loss of Cerebrospinal fluid and resection of tissue. The goal of this project is the utilization of intra-cranial 3D freehand ultrasound in order to directly estimate ocurring brain-shift and allow for a subsequent correction for it within available neur-navigation systems used for guidance in the operating room.
Ultrasound Based Tissue Characterization

Ultrasound Based Tissue Characterization

clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode ultrasound image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess lesion stage. We propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated.
Hashing forests for morphological search and retrieval in neuroscientific image databases

Hashing forests for morphological search and retrieval in neuroscientific image databases

In this project, for the first time, we propose a data-driven search and retrieval (hashing) technique for large neuron image databases. The presented method is established upon hashing forests, where multiple unsupervised random trees are used to encode neurons by parsing the neuromorphological feature space into balanced subspaces. We introduce an inverse coding formulation for retrieval of relevant neurons to effectively mitigate the need for pairwise comparisons across the database. Experimental validations show the superiority of our proposed technique over the state-of-the art methods, in terms of recall for a particular code size. This demonstrates the potential of this approach for effective morphology preserving encoding and retrieval in large neuron databases.

Student Projects

  • Implementation and development of a new service to report, analyze, visualize neurons for neuroscientists in Python (IDP, Finished).
  • Cardiac Magnetic Resonance Image Analysis and Modeling for Patients with Implantable Cardioverter Defibrillators (ICDs) (Master Thesis, Finished).
  • Integration of a WiFi? Interface and Win7 C#-GUI development for control and surveillance of a Heart Assist Device in animal experiments (IDP, Finished).
  • Knowledge propagation models for deformable image registration (Master Thesis, Finished).
  • Implementation and development of wavelet-packet-based tissue characterization for IVUS image in C++ and MeVisLab? (IDP, Finished).

Reviewer

  • Journal of Medical Image Analysis.
  • Journal of IEEE Transaction on Medical Imaging.
  • Journal of IEEE Transaction on Biomedical Engineering.
  • Journal of IEEE Transaction on Information Technology in Biomedicine.
  • Journal of Digital Signal Processing.
  • Journal of Computers in Biology and Medicine.
  • Journal of Computerized Medical Imaging and Graphics
  • Journal of Cardiovascular Imaging.
  • Journal of Cardiovascular Magnetic Resonance.
  • Medical Image Computing and Computer Assisted Intervention (MICCAI) 2013.
  • Design of Medical Devices Conference, Europe 2013.
  • International Symposium on Mixed and Augmented Reality (ISMAR) 2012.
  • Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012.
  • 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)-CVII.

Editorial Activity

  • Program Committee: International Conference on Systems in Medicine and Biology (ICSMB), Kharagpur, India, 2016.
  • Steering Committee: the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)-CVII/STENT workshop, Munich, Germany, 2015.
  • Program Committee, the MICCAI workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting MICCAI-CVII-STENT, Boston, USA, 2014.
  • Executive Committee, the 4th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), Heidelberg, Germany, 2013.
  • Program Committee, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)-CVII, Toronto, Canada, 2011.

Invited Talks

  • PicoSEC?-MCNet Training Workshop, “The necessity of personalized monitoring for patients with cardiovascular disease,” Munich, March 19th 2015.
  • Otto-von-Guericke University (Magdeburg, Germany), Computer Science Department, “Computer-assisted decision support for coronary atherosclerosis diagnosis and treatment,” February 5th, 2015.
  • Tehran University, Electrical Engineering and Computer Science Department, “Computer-assisted decision support for coronary atherosclerosis diagnosis and treatment,” January 25th, 2015.
  • MICCAI-CVII workshop, "The missing link in healthcare system; personalized monitoring," MIT Boston, 2014.
  • University of Barcelona, Computer Vision Center, "patient-Specific Translational research on Atherosclerosis and Diagnosis (STAnD?)@CAMP@TUM". February 22nd, 2013.
  • PicoSEC?-EndoTOFPET-US workshop on intraoperative imaging and navigation solutions, January 16th, 2013.
  • Technical University of Munich, Department of Mathematics, "Applications of multiscale analysis in quantification of atherosclerosis disease," November 10th, 2011.
  • Sabanci University (Istanbul, Turkey), Department of Engineering and Natural Sciences, "Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection and Plaque Characterization," December 22nd, 2010.
  • Volcano Corporation (Sacramento, CA), "Detection of Lumen Border in IVUS images via Three-Dimensional Brushlet Analysis," June 28th, 2009.

Patent

  • Ali Kamen, Lance Ladic, Sebastian Pölsterl, Adnan Kastrati, Amin Katouzian, Nassir Navab, “A novel cost-effective method for stratification of CAD patients for revascularization procedure based on minimizing the adverse effect,” 2013.
  • Debdoot Sheet, Amrita Chaudhary, Jyotirmoy Chatterjee, Ajoy Kumar Ray, Amin Katouzian, "Methods and System for Characterizing Tissues in Optical Coherence Tomography," 2013.
  • Amin Katouzian, Nassir Navab, “Intelligent Implanted Health Sensing Device and Assembly”, 2012.
  • Amin Katouzian, Andrew F. Laine, Debdoot Sheet, Athanasios Karamalis, Abouzar Eslami, Stephane G. Carlier, Nassir Navab, “System and Method for Characterizing Tissues in Intravascular Ultrasound using Statistical Physics," 2012.
  • Amin Katouzian, Elsa D. Angelini, Auranuch Lorsakul, Bernhard Sturm, Andrew F. Laine, “Denoising and Lumen Border Detection of Intravascular Ultrasound via Segmentation of Directional Wavelet Representations,” Serial No. 61/200,987, December 5, 2008.
  • Amin Katouzian, Babak Baseri, Elisa E. Konofagou, Andrew F. Laine, “Systems and Methods for Intravascular Tissue Characterization and Border Detection,” WO2009023626, 2009.
  • Amin Katouzian, Elisa E. Konofagou, Andrew F. Laine, “Unsupervised Texture-Derived Atherosclerotic Plaque Characterization Algorithm through Backscattered Intravascular Ultrasound Signals,” Serial No. 60/991,508, November 30, 2007.
  • Amin Katouzian, Elisa E. Konofagou, Andrew F. Laine, “Intravascular Ultrasound (IVUS) Tissue Characterization and Border Detection through Backscattered Signals by Wavelet Packet Analysis,” Serial No. 60/955,249, August 10, 2007.

Publication

2016
S. Pölsterl, S. Conjeti, N. Navab, A. Katouzian
Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
Artificial Intelligence in Medicine, vol. 72, pp. 1-11, 2016 (bib)
A. Shah, S. Conjeti, N. Navab, A. Katouzian
Deeply learnt hashing forests for content based image retrieval in prostate MR images
SPIE Medical Imaging, San Diego, California, USA, March 2016 (bib)
S. Conjeti, S. Mesbah, M. Negahdar, P. Rautenberg, , N. Navab, A. Katouzian
Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases
Neuroinformatics (Accepted March 2016). (bib)
S. Conjeti, A. Katouzian, A. Guha Roy, L. Peter, D. Sheet, S. Carlier, A. Laine, N. Navab
Supervised Domain Adaptation of Decision Forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization
Medical Image Analysis, 2016 (bib)
2015
S. Conjeti, S. Mesbah, A. Kumaraswamy, P. Rautenberg, N. Navab, A. Katouzian
Hashing forests for morphological search and retrieval in neuroscientific image databases
Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Munich, Germany, October 2015 (bib)
S. Pölsterl, N. Navab, A. Katouzian
Fast Training of Support Vector Machines for Survival Analysis
Machine Learning and Knowledge Discovery in Databases (bib)
J. Zimmermann, S. Rashid, H. Peng, A. Katouzian, N. Navab, D. Ennis
Cardiac MRI derived epicardial fat maps to assist VT ablation procedures for subjects with implantable devices
International Symposium on BIOMEDICAL IMAGING: From Nano to Macro, New York, USA, April 2015 (bib)
M. Yigitsoy, A. Katouzian, N. Navab
Structure Propagation for Deformable Image Stitching
International Symposium on Biomedical Imaging: From Nano to Macro, New York, USA, April 2015 (bib)
M. Yigitsoy, V. Belagiannis, A. Djurka, A. Katouzian, S. Ilic, F. Pernus, A. Eslami, N. Navab
Random Ferns for Multiple Target Tracking in Microscopic Retina Image Sequences
International Symposium on Biomedical Imaging: From Nano to Macro, New York, USA, April 2015 (bib)
D. Sheet, S. P. K. Karri, A. Katouzian, , A. K. Ray, J. Chatterjee
Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology
International Symposium on BIOMEDICAL IMAGING: From Nano to Macro, New York, USA, April 2015 (bib)
S. Pölsterl, M. Singh, A. Katouzian, N. Navab, A. Kastrati, L. Ladic, A. Kamen
Stratification of coronary artery disease patients for revascularization procedure based on estimating adverse effects
BMC Medical Informatics and Decision Making 2015, 15:9 (bib)
A. Guha Roy, S. Conjeti, S. Carlier, P. K. Dutta, A. Kastrati, A. Laine, N. Navab, A. Katouzian, D. Sheet
Lumen Segmentation in Intravascular Optical Coherence Tomography using Backscattering Tracked and Initialized Random Walks
IEEE Journal of Biomedical and Health Informatics, 2015 (In Press) (bib)
A. Guha Roy, S. Conjeti, S. Carlier, A. Konig, A. Kastrati, P. K. Dutta, A. Laine, N. Navab, D. Sheet, A. Katouzian
Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment
Proceedings of International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, April 2015 (bib)
S. Conjeti, M. Yigitsoy, D. Sheet, J. Chatterjee, N. Navab, A. Katouzian
Mutually Coherent Structural Representation for Image Registration through Joint Manifold Embedding and Alignment
Proceedings of International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, April 2015 (bib)
S. Conjeti, M. Yigitsoy, T. Peng, D. Sheet, J. Chatterjee, C. Bayer, N. Navab, A. Katouzian
Deformable Registration of immunofluorescence and Histology using iterative Cross-modal Propagation
Proceedings of International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, April 2015 (bib)
2014
R. Brosig, P. Maday, M. Kowarschik, A. Katouzian, S. Demirci, N. Navab
Blood Flow Quantification using 1D CFD Parameter Identification
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, San Diego, CA, USA, Feb 19-23 2014. (bib)
, S. Conjeti, P. B. Noël, S. Carlier, N. Navab, A. Katouzian
Full-Wave Intravascular Ultrasound Simulation from Histology
Medical Image Computing and Computer-Assisted Intervention, MICCAI, 2014 (bib)
M. Friebe, A. Katouzian, P. Matthies, S. O. Cho
Miniaturized cold emission low energy X-ray tubes for MRI guided intraoperative radiation therapy - A technical feasibility study
Poster presentation and book of abstracts, IGIC 2014, Magdeburg (bib)
M. Friebe, H.-W. Henke, P. Matthies, A. Katouzian, G. Krombach
MRI biopsy with semi-automated biopsy needle in Slicer 3D environment
Poster presentation and book of abstracts, IGIC 2014, Magdeburg (bib)
2013
D. Sheet, A. Karamalis, A. Eslami, P. B. Noël, J. Chatterjee, A. K. Ray, A. Laine, S. Carlier, N. Navab, A. Katouzian
Joint Learning of Ultrasonic Backscattering Statistical Physics and Signal Confidence Primal for Characterizing Atherosclerotic Plaques using Intravascular Ultrasound
Medial Image Analysis, 2013, (bib)
D. Sheet, , S. P. K. Karri, , A. Katouzian, , N. Navab, J. Chatterjee, A. K. Ray
In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography
Journal of Biomedical Optics, 2013. (bib)
D. Sheet, A. Karamalis, A. Eslami, P. B. Noël, , , J. Chatterjee, A. K. Ray, A. Laine, S. Carlier, N. Navab, A. Katouzian
Hunting for necrosis in the shadows of intravascular ultrasound
Special Issue, Computerized Medical Imaging and Graphics, 2013, (bib)
S. Kraft, A. Karamalis, D. Sheet, E. Drecoll, E. Rummeny, N. Navab, P. B. Noël, A. Katouzian
Introducing nuclei scatterer patterns into histology-based intravascular ultrasound simulation framework
To appear in: SPIE Medical Imaging 2013 (bib)
D. Sheet, A. Karamalis, S. Kraft, P. B. Noël, T. Vag, A. Sadhu, A. Katouzian, N. Navab, J. Chatterjee, A. K. Ray
Random Forest learning of ultrasonic statistical physics and object spaces for lesion detection in 2D sonomammography
To appear in: SPIE Medical Imaging 2013 (bib)
A. Eslami, A. Karamalis, A. Katouzian, N. Navab
Segmentation By Retrieval With Guided Random Walks: Application To Left Ventricle Segmentation in MRI
Medical Image Analysis, vol. 17, no. 2, 2013, www.sciencedirect.com.ezproxy.cul.columbia.edu/science/article/pii/S136184151200151X (bib)
2012
O. Pauly, A. Katouzian, A. Eslami, P. Fallavollita, N. Navab
Supervised Classification for Customized Intraoperative Augmented Reality Visualization
The 11th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), Atlanta, USA, Nov. 5 - 8, 2012. (bib)
A. Katouzian, , S. Carlier, , N. Navab, A. Laine
A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images
Special Issue, IEEE Transactions on Information Technology in Biomedicine, 16, 5, 823 - 834, 2012, (bib)
A. Katouzian, A. Karamalis, A. Konig, S. Carlier, N. Navab
Ambiguity in detection of necrosis in IVUS plaque characterization algorithms
European Society of Cardiology, Munich, Germany, 2012 (bib)
A. Katouzian, A. Karamalis, D. Sheet, E. Konofagou, B. Baseri, S. Carlier, A. Eslami, A. Konig, N. Navab, A. Laine
Iterative Self-Organizing Atherosclerotic Tissue Labeling in Intravascular Ultrasound Images and Comparison with Virtual Histology
IEEE Transactions on Biomedical Engineering (bib)
A. Katouzian, A. Karamalis, , A. Eslami, N. Navab
IVUS-Histology Image Registration
5th Workshop on Biomedical Image Registration, Nashville, USA, July, 2012 (bib)
A. Karamalis, A. Katouzian, S. Carlier, N. Navab
Confidence Estimation in IVUS Radio-Frequency Data with Random Walks
In Proc. 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2012 (bib)
A. Bigdelou, A. Katouzian, N. Navab
Model-based visualization of usability data to support analysis in complex domains
21th International Conference of Usability Professionals Association (UPA), Henderson, Nevada, USA, June 2012 (bib)
A. Katouzian, A. Karamalis, A. Laine, N. Navab
A Systematic Approach Toward Reliable Atherosclerotic Plaque Characterization in IVUS Images
Bildverarbeitung für die Medizin, Berlin, 2012, Germany (bib)


  • Abouzar Eslami, Amin Aboee, Zardosht Hodaei, Mandana J. Moghadam, Stephane G. Carlier, Amin Katouzian, Nassir Navab, “Quantification of Coronary Arterial Stenosis by Inflating Tubes in CT Angiographic images,” Accepted in 3D Cardiovascular Imaging: a MICCAI segmentation challenge, 2012.
  • Debdoot Sheet, Athanasios Karamalis, Nassir Navab, Andrew F. Laine, Jyotirmoy Chatterjee, Ajay K. Ray, Stephane G. Carlier, Amin Katouzian, “Machine learning of ultrasonic statistical physics primal for tissue characterization in intravascular ultrasound,” Late breaking research posters paper, 34th Annual International IEEE EMBS Conference, 2012.
  • Stephane Carlier, Athanasios Karamalis, Amin Katouzian, Nassir Navab, “Confidence estimation with random walks of IVUS based radio-frequency plaque characterization,” European Society of Cardiology, 2012.
  • Arash Taki, Alireza Roodaki, Sara Avansari, Ali Bigdelou, Amin Katouzian, Nassir Navab, “New Approaches for plaque component analysis in intravascular ultrasound (IVUS) images,” Book Chapter, Springer, Under publication.
  • Amin Katouzian, Elsa D. Angelini, Bernhard Sturm, Andrew F. Laine, “Brushlet Segmentation for Automatic Detection of Lumen Borders in IVUS Images; A Comparison Study,” Accepted for IEEE proceedings of ISBI, 2012.
  • Amin Katouzian, Stephane Carlier, “Development and performance of a new unsupervised classifier for IVUS-based tissue characterization,” The 31st annual scientific meeting of the Belgian Society of Cardiology, 2012.
  • Amin Katouzian, Elsa Angelini, Bernhard Sturm, Elisa Konofagou, Stephane Carlier, Andrew Laine, “Applications of Multiscale Overcomplete Wavelet-Based Representations in Intravascular Ultrasound (IVUS) Images,” Ultrasound Imaging; Advances and Applications, Book Chapter, Springer, 2011.
  • Amin Katouzian, Andrew F. Laine, “Methods in Atherosclerotic Plaque Characterization Using Intravascular Ultrasound (IVUS) Images and Backscattered Signals,” Atherosclerosis Disease Management, Book Chapter, Springer, pp. 121-152, 2010
  • Amin Katouzian, Elsa D. Angelini, Bernhard Sturm, Andrew F. Laine, “Automatic Detection of Luminal Borders in IVUS Images by Magnitude-Phase Histograms of Complex Brushlet Coefficients”, IEEE proceeding of EMBC, Buenos Aires, Argentina, 2010.
  • Amin Katouzian, M. Alper Selver, Elsa D. Angelini, Bernhard Sturm, Andrew F. Laine, “Classification of Blood Regions in IVUS Images Using Three Dimensional Brushlet Expansions,” IEEE proceeding of EMBC, Minnesota, 2009.
  • Amin Katouzian, Elsa D. Angelini, Auranuch Lorsakul, Bernhard Sturm, Andrew F. Laine, “Denoising and Lumen Border Detection of Intravascular Ultrasound via Segmentation of Directional Wavelet Representations,” Functional Imaging and Modeling of the Heart (FIMH), pp. 104 - 113, Nice, France, 2009.
  • Amin Katouzian, Andrew F. Laine, “Multiscale Overcomplete Wavelet-Based Representations for Classification of Atherosclerotic Plaque Using Backscattered Radiofrequency Intravascular Ultrasound Signals ", (Invited) 34th International Symposium on Ultrasonic Imaging and Tissue Characterization (UITC), 2009.
  • Amin Katouzian, Babak Baseri, Elisa E. Konofagou, Andrew F. Laine, “Texture-Driven Coronary Artery Plaque Characterization by Wavelet Packet Signatures,” IEEE proceedings of ISBI, Paris, May 2008.
  • Amin Katouzian, Babak Baseri, Elisa E. Konofagou, Andrew F. Laine, “Automatic Detection of Blood versus non-Blood Regions on Intravascular Ultrasound (IVUS) Images Using Wavelet Packet Signatures,” proceedings of SPIE, San Diego, Feb. 2008.
  • Amin Katouzian, Babak Baseri, Elisa E. Konofagou, Andrew F. Laine, “An Alternative Approach to Spectrum-Based Atherosclerortic Plaque Characterization Techniques Using Intravascular Ultrasound (IVUS) Backscattered Signals”, MICCAI-CVII 2008.
  • Amin Katouzian, Shashidhar Sathyanarayana, Babak Baseri, Elisa E. Konofagou, Stéphane G. Carlier, “Challenges in Atherosclerotic Plaque Characterization with Intravascular Ultrasound (IVUS): From Data Collection to Classification,” IEEE Transaction on Information Technology in Biomedicine, special issue on Intravascular and Intracardiac Imaging, vol. 12, no. 3, pp. 315-327, 2008.
  • Kaoru Tanaka, Stephane G. Carlier, Amin Katouzian, Gary S. Mintz, “Characterization of the Intravascular Ultrasound Radio frequency Signal within Regions of Acoustic Shadowing Behind Calcium,” Journal of American College of Cardiology, vol. 49, Issue 9, Supp. B, 2007.
  • Amin Katouzian, Shashidhar Sathyanarayana, Wenguang Li, Tom Thomas, Stéphane G. Carlier, “Challenges in Tissue Characterization from Backscattered Intravascular Ultrasound Signals,” proceedings of SPIE, San Diego, Feb. 2007.
  • Stephane G. Carlier, Kaoru Tanaka, Amin Katouzian, “Atherosclerotic Plaque Characterization from Radio Frequency Ultrasound Signal Processing,” US Cardiovascular Disease, Issue I, July 2007.
  • Amin Katouzian, A. Prakash E. Konofagou, “A New Automated Technique for Left- and Right- Ventricular segmentation in Magnetic Resonance Imaging,” IEEE proceeding of EMBC, New York, 2006.


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