DETECTION OF BREAST CANCER WITH A NEW FACTORIZATION APPROACH.

Slides:



Advertisements
Similar presentations
Principal Component Analysis Based on L1-Norm Maximization Nojun Kwak IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
Advertisements

Evaluation of Reconstruction Techniques
Optical properties of parietal peritoneum in the spectral range nm Marina D. Kozintseva 1, Alexey N. Bashkatov 1, Elina A. Genina 1, Vyacheslav.
Segmentation of Medical Images with Regional Inhomogeneities D.K. Iakovidis, M.A. Savelonas, S.A. Karkanis + & D.E. Maroulis University of Athens Department.
Modelling techniques and applications Qing Tan EPFL-STI-IMT-OPTLab
12-CRS-0106 REVISED 8 FEB 2013 Non-invasive Microwave Breast Cancer Detection - A Comparative Study Arezoo Modiri, Kamran Kiasaleh University of Texas.
Carbon Nanotubes as a Heating Agent in Microwave Ablation Introduction: Microwave Ablation (MWA) is a technique for ablating tumors using microwave frequency.
Albert Mas Ignacio Martín Gustavo Patow Fast Inverse Reflector Design FIRD Graphics Group of Girona Institut d’Informàtica i Aplicacions Universitat de.
Eigen-decomposition of a class of Infinite dimensional tridiagonal matrices G.V. Moustakides: Dept. of Computer Engineering, Univ. of Patras, Greece B.
Saratov State University ______________________________________________ Department of Optics & Biophotonics __________________________________________________.
Chapter 12 Fast Fourier Transform. 1.Metropolis algorithm for Monte Carlo 2.Simplex method for linear programming 3.Krylov subspace iteration (CG) 4.Decomposition.
Medical Image Analysis Introduction Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.
Tumor Localization Techniques Richard Kao April 10, 2001 Computer Integrated Surgery II.
Magnetic Resonance Electrical Impedance Mammography (MREIM) A new approach to breast cancer imaging Maria Kallergi, 1 John J. Heine, 2 and Ernest Wollin.
A Genetic Algorithms Approach to Feature Subset Selection Problem by Hasan Doğu TAŞKIRAN CS 550 – Machine Learning Workshop Department of Computer Engineering.
Saratov State University ______________________________________________ Department of Optics & Biophotonics __________________________________________________.
Functional Brain Signal Processing: EEG & fMRI Lesson 1 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer Authors: Guo Qingding Luo Ruifu Wang Limei IEEE IECON 22 nd International.
Parallelism and Robotics: The Perfect Marriage By R.Theron,F.J.Blanco,B.Curto,V.Moreno and F.J.Garcia University of Salamanca,Spain Rejitha Anand CMPS.
ALGORITHMIC S-Z TRANSFORMATIONS FOR CONTINUOUS-TIME TO DISCRETE- TIME FILTER CONVERSION D. Biolek, V. Biolkova Brno University of Technology Czech Republic.
Numerical ElectroMagnetics & Semiconductor Industrial Applications Ke-Ying Su Ph.D. National Central University Department of Mathematics 12 2D-NUFFT &
Numerical ElectroMagnetics & Semiconductor Industrial Applications Ke-Ying Su Ph.D. National Central University Department of Mathematics 11 NUFFT & Applications.
WAVELET TRANSFORM.
Building Three-Dimensional Images Using a Time-Reversal Chaotic Cavity
Proof of concept studies for surface-based mechanical property reconstruction 1. University of Canterbury, Christchurch, NZ 2. Eastman Kodak Company, Rochester,
Nuclear Instrumentation Laboratory Federal University of Rio de Janeiro -BRAZIL X-ray Fluorescence and X-ray Transmission Microtomography Imaging System.
Graph Data Management Lab, School of Computer Science Add title here: Large graph processing
Istanbul Technical University Electromagnetic Research Group
A Segmentation Algorithm Using Dyadic Wavelet Transform and the Discrete Dynamic Contour Bernard Chiu University of Waterloo.
1 Complex Images k’k’ k”k” k0k0 -k0-k0 branch cut   k 0 pole C1C1 C0C0 from the Sommerfeld identity, the complex exponentials must be a function.
Reconstruction of Solid Models from Oriented Point Sets Misha Kazhdan Johns Hopkins University.
Modeling Electromagnetic Fields in Strongly Inhomogeneous Media
Dortable non-invasive measurement setup of human abnormal tissue 1 Chairman:Hung-Chi Yang Presenter: Kai-Shang Chen Adviser: Jheng-Ruei Hong 1.
Ultrasound Computed Tomography 何祚明 陳彥甫 2002/06/12.
We’ve learned:. What’s next We will look at some examples and you can guess!
Mammographic image analysis for breast cancer detection using complex wavelet transforms and morphological operators.
1 EEE 431 Computational Methods in Electrodynamics Lecture 7 By Dr. Rasime Uyguroglu
Ultrasound Computed Tomography
U N I V E R S I T Y O F S O U T H F L O R I D A “ Automated Signal Processing for Data Obtained for Core Body Temperature Measurements ” Undergraduate.
Current 3D imaging systems for brain surgery are too slow to be effective in an operating room setting. All current effective methods for demarcation of.
TUMOR BURDEN ANALYSIS ON CT BY AUTOMATED LIVER AND TUMOR SEGMENTATION RAMSHEEJA.RR Roll : No 19 Guide SREERAJ.R ( Head Of Department, CSE)
By David Tse Mentor: Andreas H. Hielscher, Ph.D Columbia University Biomedical Engineering Department 500 West 120th Street ET351 Mudd Bldg., MC8904 New.
The most proven way to fight breast cancer is early detection!
Finite Element Modelling of the dipole source in EEG
“ROSATOM” STATE CORPORATION ROSATOM
ELEC 401 MICROWAVE ELECTRONICS Lecture 2
CS 591 S1 – Computational Audio
Mammogram Analysis – Tumor classification
Compression for Synthetic Aperture Sonar Signals
ELEC 401 MICROWAVE ELECTRONICS Lecture 3
Chapter 12 Fast Fourier Transform
Notes 12 ECE 6340 Intermediate EM Waves Fall 2016
Glenn Fung, Murat Dundar, Bharat Rao and Jinbo Bi
Optical Coherence Tomography
Biomedical Signal processing Chapter 1 Introduction
Monday Case of the Day Physics
Tianshuai Liu1, Junyan Rong1, Peng Gao1, Hongbing Lu1
T. Chernyakova, A. Aberdam, E. Bar-Ilan, Y. C. Eldar
Hadamard Transform Imaging
ELEC 401 MICROWAVE ELECTRONICS Lecture 3
DFT and FFT By using the complex roots of unity, we can evaluate and interpolate a polynomial in O(n lg n) An example, here are the solutions to 8 =
National Conference on Recent Advances in Wireless Communication & Artificial Intelligence (RAWCAI-2014) Organized by Department of Electronics & Communication.
Biomedical Signal processing Chapter 1 Introduction
ELEC 401 MICROWAVE ELECTRONICS Lecture 2
AN ALGORITHM FOR LOCALIZATION OF OPTICAL STRUCTURE DISTURBANCES IN BIOLOGICAL TISSUE USING TIME-RESOLVED DIFFUSE OPTICAL TOMOGRAPHY Potlov A.Yu, Frolov.
ENE 325 Electromagnetic Fields and Waves
By: Mohammad Qudeisat Supervisor: Dr. Francis Lilley
1-D DISCRETE COSINE TRANSFORM DCT
Study of Fast Ions in CESR
Biomedical Signal processing Chapter 1 Introduction
Presentation transcript:

DETECTION OF BREAST CANCER WITH A NEW FACTORIZATION APPROACH

INTRODUCTION Why we use microwave: 1. X-ray is main method a. But it causes ionization b. It has high false negative rates(%4-%34) 2. MRI is another solution a. Its resolution is high b. But over diagnosis rate is high c. Also its cost is high 3. Ultrasound has a false negative rate of %17(See [1]) Conclusion: Microwave can be a better solution in terms of cost, health risks and detection rate.

INTRODUCTION What we have done in this work: 1. We adapt new factorization approach, given in [2], to our problem. 2. Then we try to solve problem in three different configurations(Given in next slight) 3. We also proposed a new numerical approach to decrease computational complexity of direct problem.

INTRODUCTION Free space (Or space filled with just one material) Half space with lower half space filled with perfect electric conductor (PEC) Two layered media

MOTIVATION OF DIRECT AND INVERSE PROBLEMS

In general it is very hard to evaluate such integrals, but to reduce computational complexity, we used some approaches. 1) Parellel algorithms 2) Using FFT to calculate inverse Fourier transform

MOTIVATION OF DIRECT AND INVERSE PROBLEMS For FFT our works are continuing, but we achive to give some examples. The best side is algorithm complexity reduces  But it requires different techniques to solve: 1) Stability problems 2) Reduction of number of samples etc.

MOTIVATION OF DIRECT AND INVERSE PROBLEMS Here we used the new factorization method in [2]. This is different from traditional factorization method proposed in [3]. In [3]  Basis is Green functions In [2]  Basis is, so it is fast

MOTIVATION OF DIRECT AND INVERSE PROBLEMS

NUMERICAL RESULTS Breast model used is taken from real MRI data. It was a 3D model but we use some slices of it. 313*213 points -> 63*57 points or 64*64 points. Height of breast is 12cm, radius is 6cm. To add tumors we change object function at that point ( at 3 GHz)(See[4]) For obj. func. of chest we have used [5].

Config 1 – Free Space

Config 2 – PEC

Config 3 – Two Layered Medium

CONCLUSION Early detection of breast cancer has a great importance Using qualitative methods is better because: 1) It is faster 2) Its noise immunity is high But they also: 1) Requires large information for solution 2) Has some problems with their assumptions Future work: 1) 3D vectoral configurations 2) New procedures to automaticly decide for tumor…

REFERENCES [1] Review of Electromagnetic Techniques for Breast Cancer Detection” whose authors are Hassan AM, El-Shenawee M, from IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 4, [2] Y. Grisel, V. Mouysset, P.-A. Mazet, and J.-P. Raymond, “Determining the shape of defects in non-absorbing inhomogeneous media from far-field measurements,” Inverse Problems, vol. 28, no. 5, p , [3] A. Kirsch and N. Grinberg, "The Factorization Method for Inverse Problems," Oxford University Press, Oxford, [4] Lazebnik, M.; Popovic, D.; McCartney, L.; Watkins, C. B.; Lindstrom, M. J.; Harter, J.; Sewall, S.; Ogilvie, T.; Magliocco, A.; Breslin, T. M. & et al A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries Physics in Medicine and Biology, IOP Publishing, 2007, 52, [5] S. Gabriel, R. Lau, and C. Gabriel, “The dielectric properties of biological tissues: Iii. parametric models for the dielectric spectrum of tissues,” Phys. Med. Biol, vol. 41, p , 1996.

THANK YOU FOR LISTENING