Chapter 3 Set Redundancy in Magnetic Resonance Brain Images
|
|
- Joy Hubbard
- 6 years ago
- Views:
Transcription
1 16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses on brain structures but will include other image types to demonstrate our techniques. MRI systems produce brain images in cross-sections of a human head. These brain images are acquired by measuring the interaction between pulses of RF (radio frequency) radiation and tissues in a strong magnetic field. Then they are transformed to reconstruct a "3-dimensional" digital image volume. Figure 3.1 depicts the 2 slices comprising an example MR volume with 256*256 pixels in each single slice. Figure slices of MR brain images comprising an example volume
2 17 In MRI, several tissue characteristics including the PD (proton density), the longitudinal relaxation time (T1) and the transversal relaxation time (T2) can be measured. The intensity of each voxel of an MR volume image is related to the PD, T1, and T2 of the tissues located at the corresponding anatomical position. The contrast between different tissue types can be controlled at the time of acquisition by varying several MRI parameters including the pulse repetition time (T R ) and echo time (T E ). Choice of these parameters can result in PD-weighted, T1-weighted, or 59, 6 T2-weighted images. As a result, MRI data is inherently multi-spectral. In this proposed research, we use all three image types, that is, PD-weighted, T1- weighted, or T2-weighted images, as the basic test sets for proposed compression method. 3.2 Special Redundancy in a 3-D MR Brain Image Set MRI data contain large quantities of noise, which are uncorrelated from slice to slice. This makes the structure of the cross dependence more complicated than temporal sequences. Yet, a significant amount of redundancy between successive slices of MR data can be found after a close investigation of the structure of MR brain images (Figure 3.1). For instance, they are similar in terms of the shape, pixel intensity at certain anatomical position, and analogous anatomical structures from subjective observation. In addition, statistical analysis was also done to further illustrate the similarity of these images Histogram Analysis Histograms for several nonconsecutive MR brain images on Figure 3.1 are presented on Figure 3.2. This shows that more than 5 percent of the pixels have zero intensity, which corresponds to 1 percent black, and all the other intensities are uniformly distributed with a possibility of less than 1 percent.
3 18.7 Histogram of T2-Weighted Image #1.7 Histogram of T2-Weighted Image # (a) Image #1 (b) Image #6.7 Histogram of T2-Weighted Image #11.7 Histogram of T2-Weighted Image # (c) Image #11 (d) Image #16 Figure 3.2 Comparable histograms of four nonconsecutive MR brain images (Figure 3.1) Plot of Wavelet Coefficients A two-level wavelet transform was applied to the same four nonconsecutive MR brain images presented on Figure 3.2. The plots of the coefficients of all decomposed sub-images for all four images are depicted on Figure 3.3.,, V2, D2, H1, V1 and D1 represent level 2 and level 1 decompositions of wavelet transform in average, horizontal, vertical and diagonal directions respectively. The abscissa represents the vector position and the ordinate represents the value of the decomposed coefficients in their corresponding vector position. All four images have very similar (not
4 19 identical) plots for level 2 average decomposition, which is the coarse approximation of the original image at a high scale, as shown on each subplot. Deviations from the average approximation of horizontal, vertical and diagonal directions are also similar to each other V D V D H V H V D x x D x x x x 1 (a) Image #1 (b) Image # V D V D H V H V D x x D x x x x 1 (c) Image #11...(d) Image #16 Figure 3.3 Coefficients of decomposed sub-images for a 2-level wavelet transform Feature Vector Table 3.1 numerically lists the mean and variance for all decomposed sub-images presented on Figure 3.3. The corresponding vectors (MA 2, VA 2, MH 2, VH 2, MV 2, VV 2, MD 2, VD 2, MH 1, VH 1, MV 1, VV 1, MD 1, VD 1 ) derived from the first row of Table 3.1 are defined as feature vectors in our research since both mean and variance are good statistical tools to represent the general characteristics of this population. Here, MA 2,
5 2 MH 2, MV 2, MD 2, MH 1, MV 1 and MD 1 represent a mean of level 2 and level 1 decompositions of wavelet transform in average, horizontal, vertical and diagonal direction respectively, whereas VA 2, VH 2, VV 2, VD 2, VH 1, VV 1 and VD 1 represent the corresponding variances. There are only slight deviations for feature vectors of four MR brain images. MA 2 VA 2 MH 2 VH 2 MV 2 VV 2 MD 2 VD 2 MH 1 VH 1 MV 1 VV 1 MD 1 VD 1 # # # # Entropy Table 3.1 Feature vector for four nonconsecutive MR brain images The entropy for original four nonconsecutive MR brain images and their corresponding wavelet transform images are shown in Table 3.2. The entropy of an image is a measure of the amount of information an image contains, and it is also used as a measure for the compressibility of the image (lower entropy means better compressibility). The comparable entropy values for all four original MR brain images illustrate that each contains almost an equal amount of information. This holds true for the wavelet transform entropy. Image Original Entropy WT Entropy Image # Image # Image # Image # Correlation Table 3.2: Original entropy and wavelet transform entropy for four nonconsecutive MR brain images in testing image sets Finally, the correlation was determined (Table 3.3) between these same four nonconsecutive MR brain images. The existence of statistical correlation between two images can be verified graphically with a scatter plot of pixel values, and numerically by calculating the correlation coefficient. It was observed in the four
6 21 nonconsecutive MR brain images, which should have less similarity than consecutive images, that the correlation was between.6 and.7. This signifies similarities among all four images and even more similarity with neighboring MR brain images. 3.3 Summary Correlation Image #1 Image #6 Image #11 Image #16 Image # Image # Image # Image # Table 3.3 Correlation between all four nonconsecutive MR brain images Based on these statistical analyses, we can conclude that the redundancies in a 3-D MR brain image set can be summarized as follows: Similar pixel intensities in the same areas, Similar edge distributions, Analogous distributions of features, Comparable histograms (Figure 3.2), Identical sub-image coefficients of each decomposition direction and comparable feature vectors after applying transforms such as wavelet transform. (Figure 3.3 and Table 3.1), Comparable entropy for both original images and wavelet transformed images respectively (Table 3.2), High correlation (Table 3.3). The special redundancies in a 3-D MR brain image set may be expanded to any other 3-D medical image set. We will choose a 3-D knee medical image set to further demonstrate the prevalence of these redundancies.
7 22 It has been proven 61 that entropy of the image set will decrease when the redundancies of the same image set increases. Therefore, we will utilize the set redundancy in a 3-D medical image set to further the compression.
RADIOMICS: potential role in the clinics and challenges
27 giugno 2018 Dipartimento di Fisica Università degli Studi di Milano RADIOMICS: potential role in the clinics and challenges Dr. Francesca Botta Medical Physicist Istituto Europeo di Oncologia (Milano)
More informationA Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction
Tina Memo No. 2007-003 Published in Proc. MIUA 2007 A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction P. A. Bromiley and N.A. Thacker Last updated 13 / 4 / 2007 Imaging Science and
More informationWhite Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space
White Pixel Artifact Caused by a noise spike during acquisition Spike in K-space sinusoid in image space Susceptibility Artifacts Off-resonance artifacts caused by adjacent regions with different
More informationDENOISING OF COMPUTER TOMOGRAPHY IMAGES USING CURVELET TRANSFORM
VOL. 2, NO. 1, FEBRUARY 7 ISSN 1819-6608 6-7 Asian Research Publishing Network (ARPN). All rights reserved. DENOISING OF COMPUTER TOMOGRAPHY IMAGES USING CURVELET TRANSFORM R. Sivakumar Department of Electronics
More informationsurface Image reconstruction: 2D Fourier Transform
2/1/217 Chapter 2-3 K-space Intro to k-space sampling (chap 3) Frequenc encoding and Discrete sampling (chap 2) Point Spread Function K-space properties K-space sampling principles (chap 3) Basic Contrast
More informationANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma
ANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma ORAL DEFENSE 8 th of September 2017 Charlotte MARTIN Supervisor: Pr. MP REVEL M2 Bio Medical
More informationSupplementary Information
Supplementary Information Magnetic resonance imaging reveals functional anatomy and biomechanics of a living dragon tree Linnea Hesse 1,2,*, Tom Masselter 1,2,3, Jochen Leupold 4, Nils Spengler 5, Thomas
More informationSlide 1. Technical Aspects of Quality Control in Magnetic Resonance Imaging. Slide 2. Annual Compliance Testing. of MRI Systems.
Slide 1 Technical Aspects of Quality Control in Magnetic Resonance Imaging Slide 2 Compliance Testing of MRI Systems, Ph.D. Department of Radiology Henry Ford Hospital, Detroit, MI Slide 3 Compliance Testing
More informationHST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008
MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
More informationCompressed Sensing Reconstructions for Dynamic Contrast Enhanced MRI
1 Compressed Sensing Reconstructions for Dynamic Contrast Enhanced MRI Kevin T. Looby klooby@stanford.edu ABSTRACT The temporal resolution necessary for dynamic contrast enhanced (DCE) magnetic resonance
More informationCHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT
CHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT 2.1 BRIEF OUTLINE The classification of digital imagery is to extract useful thematic information which is one
More informationCorrection of Partial Volume Effects in Arterial Spin Labeling MRI
Correction of Partial Volume Effects in Arterial Spin Labeling MRI By: Tracy Ssali Supervisors: Dr. Keith St. Lawrence and Udunna Anazodo Medical Biophysics 3970Z Six Week Project April 13 th 2012 Introduction
More informationNew Technology Allows Multiple Image Contrasts in a Single Scan
These images were acquired with an investigational device. PD T2 T2 FLAIR T1 MAP T1 FLAIR PSIR T1 New Technology Allows Multiple Image Contrasts in a Single Scan MR exams can be time consuming. A typical
More informationInterpolation of 3D magnetic resonance data
Interpolation of 3D magnetic resonance data J. Mikulka 1, E. Gescheidtova 2 and K. Bartusek 3 1, 2 Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Kolejni
More informationMR IMAGE SEGMENTATION
MR IMAGE SEGMENTATION Prepared by : Monil Shah What is Segmentation? Partitioning a region or regions of interest in images such that each region corresponds to one or more anatomic structures Classification
More informationCHAPTER 3 WAVELET DECOMPOSITION USING HAAR WAVELET
69 CHAPTER 3 WAVELET DECOMPOSITION USING HAAR WAVELET 3.1 WAVELET Wavelet as a subject is highly interdisciplinary and it draws in crucial ways on ideas from the outside world. The working of wavelet in
More informationComputational Medical Imaging Analysis
Computational Medical Imaging Analysis Chapter 2: Image Acquisition Systems Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky
More informationISSN: X Impact factor: 4.295
ISSN: 2454-132X Impact factor: 4.295 (Volume3, Issue1) Available online at: www.ijariit.com Performance Analysis of Image Clustering Algorithm Applied to Brain MRI Kalyani R.Mandlik 1, Dr. Suresh S. Salankar
More informationWhole Body MRI Intensity Standardization
Whole Body MRI Intensity Standardization Florian Jäger 1, László Nyúl 1, Bernd Frericks 2, Frank Wacker 2 and Joachim Hornegger 1 1 Institute of Pattern Recognition, University of Erlangen, {jaeger,nyul,hornegger}@informatik.uni-erlangen.de
More informationOptimizing Flip Angle Selection in Breast MRI for Accurate Extraction and Visualization of T1 Tissue Relaxation Time
Optimizing Flip Angle Selection in Breast MRI for Accurate Extraction and Visualization of T1 Tissue Relaxation Time GEORGIOS KETSETZIS AND MICHAEL BRADY Medical Vision Laboratory Department of Engineering
More informationA novel noise removal using homomorphic normalization for multi-echo knee MRI
A novel noise removal using homomorphic normalization for multi-echo knee MRI Xuenan Cui 1a),HakilKim 1b), Seongwook Hong 1c), and Kyu-Sung Kwack 2d) 1 School of Information and Communication Engineering,
More informationPerformance Evaluation of the TINA Medical Image Segmentation Algorithm on Brainweb Simulated Images
Tina Memo No. 2008-003 Internal Memo Performance Evaluation of the TINA Medical Image Segmentation Algorithm on Brainweb Simulated Images P. A. Bromiley Last updated 20 / 12 / 2007 Imaging Science and
More informationVolume 2, Issue 9, September 2014 ISSN
Fingerprint Verification of the Digital Images by Using the Discrete Cosine Transformation, Run length Encoding, Fourier transformation and Correlation. Palvee Sharma 1, Dr. Rajeev Mahajan 2 1M.Tech Student
More information1 Introduction Motivation and Aims Functional Imaging Computational Neuroanatomy... 12
Contents 1 Introduction 10 1.1 Motivation and Aims....... 10 1.1.1 Functional Imaging.... 10 1.1.2 Computational Neuroanatomy... 12 1.2 Overview of Chapters... 14 2 Rigid Body Registration 18 2.1 Introduction.....
More informationA COMPARISON OF WAVELET-BASED AND RIDGELET- BASED TEXTURE CLASSIFICATION OF TISSUES IN COMPUTED TOMOGRAPHY
A COMPARISON OF WAVELET-BASED AND RIDGELET- BASED TEXTURE CLASSIFICATION OF TISSUES IN COMPUTED TOMOGRAPHY Lindsay Semler Lucia Dettori Intelligent Multimedia Processing Laboratory School of Computer Scienve,
More informationCP Generalize Concepts in Abstract Multi-dimensional Image Model Component Semantics. David Clunie.
CP-1390 - Generalize Concepts in Abstract Multi-dimensional Image Model Semantics Page 1 STATUS Date of Last Update Person Assigned Submitter Name Submission Date Assigned 2014/06/09 David Clunie mailto:dclunie@dclunie.com
More informationImage Compression Using Modified Fast Haar Wavelet Transform
World Applied Sciences Journal 7 (5): 67-653, 009 ISSN 88-95 IDOSI Publications, 009 Image Compression Using Modified Fast Haar Wavelet Transform Anuj Bhardwaj and Rashid Ali Department of Mathematics,
More informationCHAPTER 6. 6 Huffman Coding Based Image Compression Using Complex Wavelet Transform. 6.3 Wavelet Transform based compression technique 106
CHAPTER 6 6 Huffman Coding Based Image Compression Using Complex Wavelet Transform Page No 6.1 Introduction 103 6.2 Compression Techniques 104 103 6.2.1 Lossless compression 105 6.2.2 Lossy compression
More informationMedical Images Analysis and Processing
Medical Images Analysis and Processing - 25642 Emad Course Introduction Course Information: Type: Graduated Credits: 3 Prerequisites: Digital Image Processing Course Introduction Reference(s): Insight
More informationSparse sampling in MRI: From basic theory to clinical application. R. Marc Lebel, PhD Department of Electrical Engineering Department of Radiology
Sparse sampling in MRI: From basic theory to clinical application R. Marc Lebel, PhD Department of Electrical Engineering Department of Radiology Objective Provide an intuitive overview of compressed sensing
More informationINDEPENDENT COMPONENT ANALYSIS APPLIED TO fmri DATA: A GENERATIVE MODEL FOR VALIDATING RESULTS
INDEPENDENT COMPONENT ANALYSIS APPLIED TO fmri DATA: A GENERATIVE MODEL FOR VALIDATING RESULTS V. Calhoun 1,2, T. Adali, 2 and G. Pearlson 1 1 Johns Hopkins University Division of Psychiatric Neuro-Imaging,
More informationMEDICAL IMAGE ANALYSIS
SECOND EDITION MEDICAL IMAGE ANALYSIS ATAM P. DHAWAN g, A B IEEE Engineering in Medicine and Biology Society, Sponsor IEEE Press Series in Biomedical Engineering Metin Akay, Series Editor +IEEE IEEE PRESS
More informationDigital Image Processing
Digital Image Processing Intensity Transformations (Point Processing) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Intensity Transformations
More informationQuantitative MRI of the Brain: Investigation of Cerebral Gray and White Matter Diseases
Quantities Measured by MR - Quantitative MRI of the Brain: Investigation of Cerebral Gray and White Matter Diseases Static parameters (influenced by molecular environment): T, T* (transverse relaxation)
More informationMedical Image Fusion using Rayleigh Contrast Limited Adaptive Histogram Equalization and Ant Colony Edge Method
Medical Image Fusion using Rayleigh Contrast Limited Adaptive Histogram Equalization and Ant Colony Edge Method Ramandeep 1, Rajiv Kamboj 2 1 Student, M. Tech (ECE), Doon Valley Institute of Engineering
More informationExtraction and Features of Tumour from MR brain images
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 13, Issue 2, Ver. I (Mar. - Apr. 2018), PP 67-71 www.iosrjournals.org Sai Prasanna M 1,
More informationExam 8N080 - Introduction MRI
Exam 8N080 - Introduction MRI Friday January 23 rd 2015, 13.30-16.30h For this exam you may use an ordinary calculator (not a graphical one). In total there are 6 assignments and a total of 65 points can
More informationSegmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator
Segmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator Li X.C.,, Chui C. K.,, and Ong S. H.,* Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering, National
More informationMR Advance Techniques. Vascular Imaging. Class III
MR Advance Techniques Vascular Imaging Class III 1 Vascular Imaging There are several methods that can be used to evaluate the cardiovascular systems with the use of MRI. MRI will aloud to evaluate morphology
More informationIDENTIFYING different materials within sampled datasets
74 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 17, NO. 1, FEBRUARY 1998 Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data Using Voxel Histograms David H. Laidlaw,* Kurt W. Fleischer,
More informationTUMOR DETECTION IN MRI IMAGES
TUMOR DETECTION IN MRI IMAGES Prof. Pravin P. Adivarekar, 2 Priyanka P. Khatate, 3 Punam N. Pawar Prof. Pravin P. Adivarekar, 2 Priyanka P. Khatate, 3 Punam N. Pawar Asst. Professor, 2,3 BE Student,,2,3
More information2.1 Signal Production. RF_Coil. Scanner. Phantom. Image. Image Production
An Extensible MRI Simulator for Post-Processing Evaluation Remi K.-S. Kwan?, Alan C. Evans, and G. Bruce Pike McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal,
More informationCOMPREHENSIVE QUALITY CONTROL OF NMR TOMOGRAPHY USING 3D PRINTED PHANTOM
COMPREHENSIVE QUALITY CONTROL OF NMR TOMOGRAPHY USING 3D PRINTED PHANTOM Mažena MACIUSOVIČ *, Marius BURKANAS *, Jonas VENIUS *, ** * Medical Physics Department, National Cancer Institute, Vilnius, Lithuania
More informationMEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 4: Pre-Processing Medical Images (II)
SPRING 2016 1 MEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 4: Pre-Processing Medical Images (II) Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF),
More informationA Novel Iterative Thresholding Algorithm for Compressed Sensing Reconstruction of Quantitative MRI Parameters from Insufficient Data
A Novel Iterative Thresholding Algorithm for Compressed Sensing Reconstruction of Quantitative MRI Parameters from Insufficient Data Alexey Samsonov, Julia Velikina Departments of Radiology and Medical
More information7.1 INTRODUCTION Wavelet Transform is a popular multiresolution analysis tool in image processing and
Chapter 7 FACE RECOGNITION USING CURVELET 7.1 INTRODUCTION Wavelet Transform is a popular multiresolution analysis tool in image processing and computer vision, because of its ability to capture localized
More informationThe Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy
The Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy Sokratis K. Makrogiannis, PhD From post-doctoral research at SBIA lab, Department of Radiology,
More informationLearning to Identify Fuzzy Regions in Magnetic Resonance Images
Learning to Identify Fuzzy Regions in Magnetic Resonance Images Sarah E. Crane and Lawrence O. Hall Department of Computer Science and Engineering, ENB 118 University of South Florida 4202 E. Fowler Ave.
More informationConstructing System Matrices for SPECT Simulations and Reconstructions
Constructing System Matrices for SPECT Simulations and Reconstructions Nirantha Balagopal April 28th, 2017 M.S. Report The University of Arizona College of Optical Sciences 1 Acknowledgement I would like
More informationThis exercise uses one anatomical data set (ANAT1) and two functional data sets (FUNC1 and FUNC2).
Exploring Brain Anatomy This week s exercises will let you explore the anatomical organization of the brain to learn some of its basic properties, as well as the location of different structures. The human
More informationImage Acquisition Systems
Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS
More informationImage Processing Techniques for Brain Tumor Extraction from MRI Images using SVM Classifier
Image Processing Techniques for Brain Tumor Extraction from MRI Images using SVM Classifier Mr. Ajaj Khan M. Tech (CSE) Scholar Central India Institute of Technology Indore, India ajajkhan72@gmail.com
More informationSeparate CT-Reconstruction for Orientation and Position Adaptive Wavelet Denoising
Separate CT-Reconstruction for Orientation and Position Adaptive Wavelet Denoising Anja Borsdorf 1,, Rainer Raupach, Joachim Hornegger 1 1 Chair for Pattern Recognition, Friedrich-Alexander-University
More informationMedical Image Compression using DCT and DWT Techniques
Medical Image Compression using DCT and DWT Techniques Gullanar M. Hadi College of Engineering-Software Engineering Dept. Salahaddin University-Erbil, Iraq gullanarm@yahoo.com ABSTRACT In this paper we
More informationBasic principles of MR image analysis. Basic principles of MR image analysis. Basic principles of MR image analysis
Basic principles of MR image analysis Basic principles of MR image analysis Julien Milles Leiden University Medical Center Terminology of fmri Brain extraction Registration Linear registration Non-linear
More informationMRI Physics II: Gradients, Imaging
MRI Physics II: Gradients, Imaging Douglas C., Ph.D. Dept. of Biomedical Engineering University of Michigan, Ann Arbor Magnetic Fields in MRI B 0 The main magnetic field. Always on (0.5-7 T) Magnetizes
More informationAnalysis of Functional MRI Timeseries Data Using Signal Processing Techniques
Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Sea Chen Department of Biomedical Engineering Advisors: Dr. Charles A. Bouman and Dr. Mark J. Lowe S. Chen Final Exam October
More informationFUSION OF TWO IMAGES BASED ON WAVELET TRANSFORM
FUSION OF TWO IMAGES BASED ON WAVELET TRANSFORM Pavithra C 1 Dr. S. Bhargavi 2 Student, Department of Electronics and Communication, S.J.C. Institute of Technology,Chickballapur,Karnataka,India 1 Professor,
More informationHST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006
MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
More informationThe University of Chicago. Center for EPR Imaging in Vivo Physiology. Image Registration. Boris Epel
The University of Chicago Center for EPR Imaging in Vivo Physiology Image Registration Boris Epel Imaging Methods are Complimentary CT MRI EPRI High resolution anatomic images Quantitative Poor soft tissue
More informationField Maps. 1 Field Map Acquisition. John Pauly. October 5, 2005
Field Maps John Pauly October 5, 25 The acquisition and reconstruction of frequency, or field, maps is important for both the acquisition of MRI data, and for its reconstruction. Many of the imaging methods
More informationRole of Parallel Imaging in High Field Functional MRI
Role of Parallel Imaging in High Field Functional MRI Douglas C. Noll & Bradley P. Sutton Department of Biomedical Engineering, University of Michigan Supported by NIH Grant DA15410 & The Whitaker Foundation
More informationLocating Motion Artifacts in Parametric fmri Analysis
Tina Memo No. 200-002 Presented at MICCAI 999 Locating Motion Artifacts in Parametric fmri Analysis A.J.Lacey, N.A.Thacker, E. Burton, and A.Jackson Last updated 2 / 02 / 2002 Imaging Science and Biomedical
More informationCHAPTER 9: Magnetic Susceptibility Effects in High Field MRI
Figure 1. In the brain, the gray matter has substantially more blood vessels and capillaries than white matter. The magnified image on the right displays the rich vasculature in gray matter forming porous,
More informationImage Registration. Prof. Dr. Lucas Ferrari de Oliveira UFPR Informatics Department
Image Registration Prof. Dr. Lucas Ferrari de Oliveira UFPR Informatics Department Introduction Visualize objects inside the human body Advances in CS methods to diagnosis, treatment planning and medical
More informationA Study of Medical Image Analysis System
Indian Journal of Science and Technology, Vol 8(25), DOI: 10.17485/ijst/2015/v8i25/80492, October 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 A Study of Medical Image Analysis System Kim Tae-Eun
More informationCHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS
130 CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS A mass is defined as a space-occupying lesion seen in more than one projection and it is described by its shapes and margin
More informationAbbie M. Diak, PhD Loyola University Medical Center Dept. of Radiation Oncology
Abbie M. Diak, PhD Loyola University Medical Center Dept. of Radiation Oncology Outline High Spectral and Spatial Resolution MR Imaging (HiSS) What it is How to do it Ways to use it HiSS for Radiation
More informationIntensity Transformations. Digital Image Processing. What Is Image Enhancement? Contents. Image Enhancement Examples. Intensity Transformations
Digital Image Processing 2 Intensity Transformations Intensity Transformations (Point Processing) Christophoros Nikou cnikou@cs.uoi.gr It makes all the difference whether one sees darkness through the
More informationDigital Volume Correlation for Materials Characterization
19 th World Conference on Non-Destructive Testing 2016 Digital Volume Correlation for Materials Characterization Enrico QUINTANA, Phillip REU, Edward JIMENEZ, Kyle THOMPSON, Sharlotte KRAMER Sandia National
More informationNorbert Schuff VA Medical Center and UCSF
Norbert Schuff Medical Center and UCSF Norbert.schuff@ucsf.edu Medical Imaging Informatics N.Schuff Course # 170.03 Slide 1/67 Objective Learn the principle segmentation techniques Understand the role
More informationCHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE
32 CHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE 3.1 INTRODUCTION In this chapter we present the real time implementation of an artificial neural network based on fuzzy segmentation process
More informationDosimetric Analysis Report
RT-safe 48, Artotinis str 116 33, Athens Greece +30 2107563691 info@rt-safe.com Dosimetric Analysis Report SAMPLE, for demonstration purposes only Date of report: ----------- Date of irradiation: -----------
More information6 credits. BMSC-GA Practical Magnetic Resonance Imaging II
BMSC-GA 4428 - Practical Magnetic Resonance Imaging II 6 credits Course director: Ricardo Otazo, PhD Course description: This course is a practical introduction to image reconstruction, image analysis
More informationStatistical Analysis of MRI Data
Statistical Analysis of MRI Data Shelby Cummings August 1, 2012 Abstract Every day, numerous people around the country go under medical testing with the use of MRI technology. Developed in the late twentieth
More informationTERM PAPER ON The Compressive Sensing Based on Biorthogonal Wavelet Basis
TERM PAPER ON The Compressive Sensing Based on Biorthogonal Wavelet Basis Submitted By: Amrita Mishra 11104163 Manoj C 11104059 Under the Guidance of Dr. Sumana Gupta Professor Department of Electrical
More informationDenoising the Spectral Information of Non Stationary Image using DWT
Denoising the Spectral Information of Non Stationary Image using DWT Dr.DolaSanjayS 1, P. Geetha Lavanya 2, P.Jagapathi Raju 3, M.Sai Kishore 4, T.N.V.Krishna Priya 5 1 Principal, Ramachandra College of
More informationWavelet-based Texture Classification of Tissues in Computed Tomography
Wavelet-based Texture Classification of Tissues in Computed Tomography Lindsay Semler, Lucia Dettori, Jacob Furst Intelligent Multimedia Processing Laboratory School of Computer Science, Telecommunications,
More informationClassification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging
1 CS 9 Final Project Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging Feiyu Chen Department of Electrical Engineering ABSTRACT Subject motion is a significant
More informationSkull Segmentation of MR images based on texture features for attenuation correction in PET/MR
Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR CHAIBI HASSEN, NOURINE RACHID ITIO Laboratory, Oran University Algeriachaibih@yahoo.fr, nourine@yahoo.com
More informationIntroduction to Medical Image Processing
Introduction to Medical Image Processing Δ Essential environments of a medical imaging system Subject Image Analysis Energy Imaging System Images Image Processing Feature Images Image processing may be
More informationarxiv: v2 [q-bio.qm] 16 Oct 2017
Gulban this is page 1 The relation between color spaces and compositional data analysis demonstrated with magnetic resonance image processing applications O.F. Gulban Maastricht University, Maastricht,
More informationReview and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding.
Project Title: Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding. Midterm Report CS 584 Multimedia Communications Submitted by: Syed Jawwad Bukhari 2004-03-0028 About
More informationSupplementary Figure 1
Supplementary Figure 1 BOLD and CBV functional maps showing EPI versus line-scanning FLASH fmri. A. Colored BOLD and CBV functional maps are shown in the highlighted window (green frame) of the raw EPI
More informationAutomated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images
University of Miami Scholarly Repository Open Access Dissertations Electronic Theses and Dissertations 2015-05-04 Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images Nooshin
More informationIMAGE DIGITIZATION BY WAVELET COEFFICIENT WITH HISTOGRAM SHAPING AND SPECIFICATION
IMAGE DIGITIZATION BY WAVELET COEFFICIENT WITH HISTOGRAM SHAPING AND SPECIFICATION Shivam Sharma 1, Mr. Lalit Singh 2 1,2 M.Tech Scholor, 2 Assistant Professor GRDIMT, Dehradun (India) ABSTRACT Many applications
More informationBiometric Security System Using Palm print
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference
More informationImage Denoising Based on Hybrid Fourier and Neighborhood Wavelet Coefficients Jun Cheng, Songli Lei
Image Denoising Based on Hybrid Fourier and Neighborhood Wavelet Coefficients Jun Cheng, Songli Lei College of Physical and Information Science, Hunan Normal University, Changsha, China Hunan Art Professional
More informationDiffusion model fitting and tractography: A primer
Diffusion model fitting and tractography: A primer Anastasia Yendiki HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging 03/18/10 Why n how Diffusion model fitting and tractography 0/18 Why
More informationMedical Imaging Projects
NSF REU MedIX Summer 2006 Medical Imaging Projects Daniela Stan Raicu, PhD http://facweb.cs.depaul.edu/research draicu@cs.depaul.edu Outline Medical Informatics Imaging Modalities Computed Tomography Medical
More informationNON-LINEAR MEDIAN FILTERING OF BIOMEDICAL IMAGES
O-LIEAR MEDIA FILTERIG OF BIOMEDICAL IMAGES V. Musoko 1 and A. Procházka 1 1 Institute of Chemical Technology, Department of Computing and Control Engineering Abstract The paper presents basic principles
More informationOptimal Sampling Geometries for TV-Norm Reconstruction of fmri Data
Optimal Sampling Geometries for TV-Norm Reconstruction of fmri Data Oliver M. Jeromin, Student Member, IEEE, Vince D. Calhoun, Senior Member, IEEE, and Marios S. Pattichis, Senior Member, IEEE Abstract
More informationPET AND MRI BRAIN IMAGE FUSION USING REDUNDANT WAVELET TRANSFORM
International Journal of Latest Engineering and Management Research (IJLEMR) ISSN: 2455-4847 Volume 1 Issue 4 ǁ May 2016 ǁ PP.21-26 PET AND MRI BRAIN IMAGE FUSION USING REDUNDANT WAVELET TRANSFORM Gayathri
More informationAn Introduction To Automatic Tissue Classification Of Brain MRI. Colm Elliott Mar 2014
An Introduction To Automatic Tissue Classification Of Brain MRI Colm Elliott Mar 2014 Tissue Classification Tissue classification is part of many processing pipelines. We often want to classify each voxel
More informationIntroduction to fmri. Pre-processing
Introduction to fmri Pre-processing Tibor Auer Department of Psychology Research Fellow in MRI Data Types Anatomical data: T 1 -weighted, 3D, 1/subject or session - (ME)MPRAGE/FLASH sequence, undistorted
More informationCh. 4 Physical Principles of CT
Ch. 4 Physical Principles of CT CLRS 408: Intro to CT Department of Radiation Sciences Review: Why CT? Solution for radiography/tomography limitations Superimposition of structures Distinguishing between
More informationIMAGE COMPRESSION. Chapter - 5 : (Basic)
Chapter - 5 : IMAGE COMPRESSION (Basic) Q() Explain the different types of redundncies that exists in image.? (8M May6 Comp) [8M, MAY 7, ETRX] A common characteristic of most images is that the neighboring
More informationM R I Physics Course
M R I Physics Course Multichannel Technology & Parallel Imaging Nathan Yanasak, Ph.D. Jerry Allison Ph.D. Tom Lavin, B.S. Department of Radiology Medical College of Georgia References: 1) The Physics of
More informationLucy Phantom MR Grid Evaluation
Lucy Phantom MR Grid Evaluation Anil Sethi, PhD Loyola University Medical Center, Maywood, IL 60153 November 2015 I. Introduction: The MR distortion grid, used as an insert with Lucy 3D QA phantom, is
More informationSupplementary methods
Supplementary methods This section provides additional technical details on the sample, the applied imaging and analysis steps and methods. Structural imaging Trained radiographers placed all participants
More information