Math in image processing
|
|
- Bruce Norris
- 6 years ago
- Views:
Transcription
1 Math in image processing
2 Math in image processing Nyquist theorem
3 Math in image processing Discrete Fourier Transformation
4 Math in image processing Image enhancement: scaling
5 Math in image processing Image enhancement: histogram equalization cumulative histogram improved image
6 Math in image processing Image enhancement: filtering (low- or high-pass) Reducing the amplitudes of low-freq peak we can avoid some of the artefacts.
7 Math in image processing: segmentation An idea is to find the clusters or subsets of the image which can be considered (or its characteristics) as homogeneous. skull CSF (cerebrospinal fluid) White matter or Grey matter
8 Math in image processing: segmentation First and simple way to do it manually (frequently is applied, for example, in the case of tumour segmentation).
9 Math in image processing: segmentation Thresholding
10 Edge-based segmentation Edges MRA Direction High threshold edges Low threshold edges Gradient Math in image processing: segmentation
11 Math in image processing: segmentation FSL: BET Fslview allows one to visualise the results in three projections
12 Math in image processing: segmentation FSL: BET Artefacts from bad parametrisation
13 Math in image processing: segmentation FSL: BET Extracted/rendered brain Extracted/rendered skull
14 Math in image processing: segmentation FSL: FAST WM CSF GM Rendered T1 raw data
15 Math in image processing: segmentation FreeSurfer How to segment properly the GM
16 Math in image processing: segmentation FreeSurfer Convert it into computer model
17 Math in image processing: registration Cerebral cortex can be segmented in special regions (Broadmann, 1909). The question: how can we compare the same regions for different subjects?
18 Math in image processing: registration Idea is to find a transformation T which allows one to align two images. One image is fixed, when another is moving. where S is a similarity, P is a penalty Different variants of the similarity functions: Sum of squared differences Mutual information
19 Math in image processing: registration What kind of geometrical transformation we can use? Rotation (rigid body transformation)
20 Math in image processing: registration What kind of geometrical transformation we can use? Rotation Translation (rigid body transformation)
21 Math in image processing: registration What kind of geometrical transformation we can use? Rotation Translation Scaling (nonrigid)
22 Math in image processing: registration Simple 2D case The same case but generalized to 3D
23 Math in image processing: registration
24 Math in image processing: registration Affine transformation: wiki page example
25 Math in image processing: registration Non-linear transformations
26 Math in image processing: registration Non-linear transformations: often before it we do affine transformation Warp functions This transformation is reversible!
27 Math in image processing: fitting Y We need to fit the measured data to model function X
28 Math in image processing: fitting Y We need to fit the measured data to model function If function is linear it's more or less easy to do: y = ax + b X
29 Math in image processing: fitting Y We need to fit the measured data to model function If function is linear it's more or less easy to do: y = ax + b X Outlier
30 Math in image processing: fitting Robust estimators in regression methods
31 Problems 1. Install FSL ( and try different utilities from the package. 2. Install SPM ( and try different utilities from the package. 3. Install FreeSurfer ( and try different utilities form the package. 4. Install ITK-SNAP, try to segment the images from the given examples. 5. Extract the brain using BET utility with minimal artefacts 6. Extract WM, GM, and CSF tissues using FAST with minimal artefacts 7. Are the unit transformation in registration procedure commutative? 8. Perform a coregistration of 4D volumes of diffusion dataset
32 Literature Bankman, Handbook of medical imaging. Processing and analysis Smith, Digital signal processing Sonka and Fitzpattrick, Handbook of medical imaging, vol.2. Medical image processing and analysis
33 Next topic is Statistics
Automated MR Image Analysis Pipelines
Automated MR Image Analysis Pipelines Andy Simmons Centre for Neuroimaging Sciences, Kings College London Institute of Psychiatry. NIHR Biomedical Research Centre for Mental Health at IoP & SLAM. Neuroimaging
More informationNon-rigid Image Registration using Electric Current Flow
Non-rigid Image Registration using Electric Current Flow Shu Liao, Max W. K. Law and Albert C. S. Chung Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering,
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 informationFunctional MRI data preprocessing. Cyril Pernet, PhD
Functional MRI data preprocessing Cyril Pernet, PhD Data have been acquired, what s s next? time No matter the design, multiple volumes (made from multiple slices) have been acquired in time. Before getting
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 informationEnsemble registration: Combining groupwise registration and segmentation
PURWANI, COOTES, TWINING: ENSEMBLE REGISTRATION 1 Ensemble registration: Combining groupwise registration and segmentation Sri Purwani 1,2 sri.purwani@postgrad.manchester.ac.uk Tim Cootes 1 t.cootes@manchester.ac.uk
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 informationMethods for data preprocessing
Methods for data preprocessing John Ashburner Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Overview Voxel-Based Morphometry Morphometry in general Volumetrics VBM preprocessing
More informationComparison Study of Clinical 3D MRI Brain Segmentation Evaluation
Comparison Study of Clinical 3D MRI Brain Segmentation Evaluation Ting Song 1, Elsa D. Angelini 2, Brett D. Mensh 3, Andrew Laine 1 1 Heffner Biomedical Imaging Laboratory Department of Biomedical Engineering,
More informationASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL
ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL ASAP was developed as part of the COST Action "Arterial Spin Labelling Initiative in Dementia (AID)" by: Department of Neuroimaging, Institute
More informationHow to create a head model
How to create a head model This document describes the command line tools: mri2mesh: Central tool to reconstruct a head model from T1w and T2w data dwi2cond: Reconstruct conductivity tensors for brain
More informationADAPTIVE GRAPH CUTS WITH TISSUE PRIORS FOR BRAIN MRI SEGMENTATION
ADAPTIVE GRAPH CUTS WITH TISSUE PRIORS FOR BRAIN MRI SEGMENTATION Abstract: MIP Project Report Spring 2013 Gaurav Mittal 201232644 This is a detailed report about the course project, which was to implement
More informationComputational Neuroanatomy
Computational Neuroanatomy John Ashburner john@fil.ion.ucl.ac.uk Smoothing Motion Correction Between Modality Co-registration Spatial Normalisation Segmentation Morphometry Overview fmri time-series kernel
More informationEMSegmenter Tutorial (Advanced Mode)
EMSegmenter Tutorial (Advanced Mode) Dominique Belhachemi Section of Biomedical Image Analysis Department of Radiology University of Pennsylvania 1/65 Overview The goal of this tutorial is to apply the
More informationGLM for fmri data analysis Lab Exercise 1
GLM for fmri data analysis Lab Exercise 1 March 15, 2013 Medical Image Processing Lab Medical Image Processing Lab GLM for fmri data analysis Outline 1 Getting Started 2 AUDIO 1 st level Preprocessing
More informationKnowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit
Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit John Melonakos 1, Ramsey Al-Hakim 1, James Fallon 2 and Allen Tannenbaum 1 1 Georgia Institute of Technology, Atlanta GA 30332,
More informationCS664 Lecture #16: Image registration, robust statistics, motion
CS664 Lecture #16: Image registration, robust statistics, motion Some material taken from: Alyosha Efros, CMU http://www.cs.cmu.edu/~efros Xenios Papademetris http://noodle.med.yale.edu/~papad/various/papademetris_image_registration.p
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 informationHST.582J / 6.555J / J Biomedical Signal and Image Processing Spring 2007
MIT OpenCourseWare http://ocw.mit.edu HST.582J / 6.555J / 16.456J Biomedical Signal and Image Processing Spring 2007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
More informationMRI Segmentation MIDAS, 2007, 2010
MRI Segmentation MIDAS, 2007, 2010 Lawrence O. Hall, Dmitry Goldgof, Yuhua Gu, Prodip Hore Dept. of Computer Science & Engineering University of South Florida CONTENTS: 1. Introduction... 1 2. Installing
More informationImage Registration + Other Stuff
Image Registration + Other Stuff John Ashburner Pre-processing Overview fmri time-series Motion Correct Anatomical MRI Coregister m11 m 21 m 31 m12 m13 m14 m 22 m 23 m 24 m 32 m 33 m 34 1 Template Estimate
More informationKnowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit
Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit John Melonakos 1, Ramsey Al-Hakim 1, James Fallon 2 and Allen Tannenbaum 1 1 Georgia Institute of Technology, Atlanta GA 30332,
More informationAFNI Preprocessing: Outline, Recommendations, and New(ish) Stuff. Robert W Cox SSCC / NIMH & NINDS / NIH / DHHS / USA / EARTH
AFNI Preprocessing: Outline, Recommendations, and New(ish) Stuff Robert W Cox SSCC / NIMH & NINDS / NIH / DHHS / USA / EARTH HBM 2016 As a work of a US Government official, this presentation is not copyrighted
More informationThe organization of the human cerebral cortex estimated by intrinsic functional connectivity
1 The organization of the human cerebral cortex estimated by intrinsic functional connectivity Journal: Journal of Neurophysiology Author: B. T. Thomas Yeo, et al Link: https://www.ncbi.nlm.nih.gov/pubmed/21653723
More informationAdvanced Visual Medicine: Techniques for Visual Exploration & Analysis
Advanced Visual Medicine: Techniques for Visual Exploration & Analysis Interactive Visualization of Multimodal Volume Data for Neurosurgical Planning Felix Ritter, MeVis Research Bremen Multimodal Neurosurgical
More informationIS MRI Based Structure a Mediator for Lead s Effect on Cognitive Function?
IS MRI Based Structure a Mediator for Lead s Effect on Cognitive Function? Brian Caffo, Sining Chen, Brian Schwartz Department of Biostatistics and Environmental Health Sciences Johns Hopkins University
More informationNonrigid Registration using Free-Form Deformations
Nonrigid Registration using Free-Form Deformations Hongchang Peng April 20th Paper Presented: Rueckert et al., TMI 1999: Nonrigid registration using freeform deformations: Application to breast MR images
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 informationImage Registration I
Image Registration I Comp 254 Spring 2002 Guido Gerig Image Registration: Motivation Motivation for Image Registration Combine images from different modalities (multi-modality registration), e.g. CT&MRI,
More informationFSL Pre-Processing Pipeline
The Art and Pitfalls of fmri Preprocessing FSL Pre-Processing Pipeline Mark Jenkinson FMRIB Centre, University of Oxford FSL Pre-Processing Pipeline Standard pre-processing: Task fmri Resting-state fmri
More informationGeometric Modeling and Processing
Geometric Modeling and Processing Tutorial of 3DIM&PVT 2011 (Hangzhou, China) May 16, 2011 4. Geometric Registration 4.1 Rigid Registration Range Scanning: Reconstruction Set of raw scans Reconstructed
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 informationSegmentation of the neonatal brain for MR images A REVIEW
Segmentation of the neonatal brain for MR images A REVIEW Tineke Hoppinga 19-5-2010 Inhoud Abbreviations... 4 Abstract... 5 Introduction... 6 Method... 8 Basic explanations of the methods... 8 Weisenfeld...
More informationDiffusion-MRI processing for group analysis
Diffusion-MRI processing for group analysis Felix Renard IRMaGe: Inserm US 17 / CNRS UMS 3552 University Hospital of Grenoble - France 25/09/2015 felixrenard@gmail.com 1 Diffusion-MRI processing for group
More informationSurface-based Analysis: Inter-subject Registration and Smoothing
Surface-based Analysis: Inter-subject Registration and Smoothing Outline Exploratory Spatial Analysis Coordinate Systems 3D (Volumetric) 2D (Surface-based) Inter-subject registration Volume-based Surface-based
More informationFSL Pre-Processing Pipeline
The Art and Pitfalls of fmri Preprocessing FSL Pre-Processing Pipeline Mark Jenkinson FMRIB Centre, University of Oxford FSL Pre-Processing Pipeline Standard pre-processing: Task fmri Resting-state fmri
More informationFROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING. Francesca Pizzorni Ferrarese
FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING Francesca Pizzorni Ferrarese Pipeline overview WM and GM Segmentation Registration Data reconstruction Tractography
More informationNorbert Schuff Professor of Radiology VA Medical Center and UCSF
Norbert Schuff Professor of Radiology Medical Center and UCSF Norbert.schuff@ucsf.edu 2010, N.Schuff Slide 1/67 Overview Definitions Role of Segmentation Segmentation methods Intensity based Shape based
More informationRegistration by continuous optimisation. Stefan Klein Erasmus MC, the Netherlands Biomedical Imaging Group Rotterdam (BIGR)
Registration by continuous optimisation Stefan Klein Erasmus MC, the Netherlands Biomedical Imaging Group Rotterdam (BIGR) Registration = optimisation C t x t y 1 Registration = optimisation C t x t y
More informationAutomatic Generation of Training Data for Brain Tissue Classification from MRI
Automatic Generation of Training Data for Brain Tissue Classification from MRI Chris A. COCOSCO, Alex P. ZIJDENBOS, and Alan C. EVANS http://www.bic.mni.mcgill.ca/users/crisco/ McConnell Brain Imaging
More informationWhere are we now? Structural MRI processing and analysis
Where are we now? Structural MRI processing and analysis Pierre-Louis Bazin bazin@cbs.mpg.de Leipzig, Germany Structural MRI processing: why bother? Just use the standards? SPM FreeSurfer FSL However:
More informationIschemic Stroke Lesion Segmentation Proceedings 5th October 2015 Munich, Germany
0111010001110001101000100101010111100111011100100011011101110101101012 Ischemic Stroke Lesion Segmentation www.isles-challenge.org Proceedings 5th October 2015 Munich, Germany Preface Stroke is the second
More informationAutomatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos
Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos Jue Wu and Brian Avants Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, USA Abstract.
More informationLecture 13 Theory of Registration. ch. 10 of Insight into Images edited by Terry Yoo, et al. Spring (CMU RI) : BioE 2630 (Pitt)
Lecture 13 Theory of Registration ch. 10 of Insight into Images edited by Terry Yoo, et al. Spring 2018 16-725 (CMU RI) : BioE 2630 (Pitt) Dr. John Galeotti The content of these slides by John Galeotti,
More informationIntroduction to MRI data processing with FSL. Anna Blazejewska
Introduction to MRI data processing with FSL Anna Blazejewska FSL = FMRIB Software Library FMRIB = Functional Magnetic Resonance Imaging of the Brain @ Oxford since 2000, last stable FSL 5.0, free! for
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 informationSubvoxel Segmentation and Representation of Brain Cortex Using Fuzzy Clustering and Gradient Vector Diffusion
Subvoxel Segmentation and Representation of Brain Cortex Using Fuzzy Clustering and Gradient Vector Diffusion Ming-Ching Chang Xiaodong Tao GE Global Research Center {changm, taox} @ research.ge.com SPIE
More informationHistograms. h(r k ) = n k. p(r k )= n k /NM. Histogram: number of times intensity level rk appears in the image
Histograms h(r k ) = n k Histogram: number of times intensity level rk appears in the image p(r k )= n k /NM normalized histogram also a probability of occurence 1 Histogram of Image Intensities Create
More informationRobust Realignment of fmri Time Series Data
Robust Realignment of fmri Time Series Data Ben Dodson bjdodson@stanford.edu Olafur Gudmundsson olafurg@stanford.edu December 12, 2008 Abstract FMRI data has become an increasingly popular source for exploring
More informationAtlas of Classifiers for Brain MRI Segmentation
Atlas of Classifiers for Brain MRI Segmentation B. Kodner 1,2, S. H. Gordon 1,2, J. Goldberger 3 and T. Riklin Raviv 1,2 1 Department of Electrical and Computer Engineering, 2 The Zlotowski Center for
More informationPreprocessing II: Between Subjects John Ashburner
Preprocessing II: Between Subjects John Ashburner Pre-processing Overview Statistics or whatever fmri time-series Anatomical MRI Template Smoothed Estimate Spatial Norm Motion Correct Smooth Coregister
More informationCV: Matching in 2D. Matching 2D images to 2D images; Matching 2D images to 2D maps or 2D models; Matching 2D maps to 2D maps MSU CSE 803
CV: Matching in 2D Matching 2D images to 2D images; Matching 2D images to 2D maps or 2D models; Matching 2D maps to 2D maps 2D Matching n n Problem 1) Need to match images to maps or models 2) need to
More informationNorbert Schuff Professor of Radiology VA Medical Center and UCSF
Norbert Schuff Professor of Radiology Medical Center and UCSF Norbert.schuff@ucsf.edu Slide 1/67 Overview Definitions Role of Segmentation Segmentation methods Intensity based Shape based Texture based
More informationMultiple Sclerosis Brain MRI Segmentation Workflow deployment on the EGEE grid
Multiple Sclerosis Brain MRI Segmentation Workflow deployment on the EGEE grid Erik Pernod 1, Jean-Christophe Souplet 1, Javier Rojas Balderrama 2, Diane Lingrand 2, Xavier Pennec 1 Speaker: Grégoire Malandain
More informationQUANTITATION OF THE PREMATURE INFANT BRAIN VOLUME FROM MR IMAGES USING WATERSHED TRANSFORM AND BAYESIAN SEGMENTATION
QUANTITATION OF THE PREMATURE INFANT BRAIN VOLUME FROM MR IMAGES USING WATERSHED TRANSFORM AND BAYESIAN SEGMENTATION Merisaari Harri 1;2, Teräs Mika 2, Alhoniemi Esa 1, Parkkola Riitta 2;3, Nevalainen
More informationDEVELOPMENT OF REALISTIC HEAD MODELS FOR ELECTRO- MAGNETIC SOURCE IMAGING OF THE HUMAN BRAIN
DEVELOPMENT OF REALISTIC HEAD MODELS FOR ELECTRO- MAGNETIC SOURCE IMAGING OF THE HUMAN BRAIN Z. "! #$! Acar, N.G. Gençer Department of Electrical and Electronics Engineering, Middle East Technical University,
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 informationAccurate Image Registration from Local Phase Information
Accurate Image Registration from Local Phase Information Himanshu Arora, Anoop M. Namboodiri, and C.V. Jawahar Center for Visual Information Technology, IIIT, Hyderabad, India { himanshu@research., anoop@,
More informationBrain Extraction, Registration & EPI Distortion Correction
Brain Extraction, Registration & EPI Distortion Correction What use is Registration? Some common uses of registration: Combining across individuals in group studies: including fmri & diffusion Quantifying
More informationNeuroimaging and mathematical modelling Lesson 2: Voxel Based Morphometry
Neuroimaging and mathematical modelling Lesson 2: Voxel Based Morphometry Nivedita Agarwal, MD Nivedita.agarwal@apss.tn.it Nivedita.agarwal@unitn.it Volume and surface morphometry Brain volume White matter
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 informationA Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms
A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms Omar Hamo, Georg Nelles, Gudrun Wagenknecht Central Institute for Electronics, Research Center Juelich,
More informationThis Time. fmri Data analysis
This Time Reslice example Spatial Normalization Noise in fmri Methods for estimating and correcting for physiologic noise SPM Example Spatial Normalization: Remind ourselves what a typical functional image
More informationApplication of fuzzy set theory in image analysis
Application of fuzzy set theory in image analysis Analysis and defuzzification of discrete fuzzy spatial sets Who am I? Nataša Sladoje Teaching assistant in mathematics Department of fundamental disciplines
More informationMedical Image Synthesis Methods and Applications
MR Intensity Scale is Arbitrary This causes problems in most postprocessing methods Inconsistency or algorithm failure 11/5/2015 2 Joint Histogram 1.5 T GE SPGR 3 T Philips MPRAGE 11/5/2015 3 Problem With
More informationA NEW FEATURE BASED IMAGE REGISTRATION ALGORITHM INTRODUCTION
A NEW FEATURE BASED IMAGE REGISTRATION ALGORITHM Karthik Krish Stuart Heinrich Wesley E. Snyder Halil Cakir Siamak Khorram North Carolina State University Raleigh, 27695 kkrish@ncsu.edu sbheinri@ncsu.edu
More informationJournal of Articles in Support of The Null Hypothesis
Data Preprocessing Martin M. Monti, PhD UCLA Psychology NITP 2016 Typical (task-based) fmri analysis sequence Image Pre-processing Single Subject Analysis Group Analysis Journal of Articles in Support
More informationEPILOG PREOP. Viewer Interpretation Guideline
EPILOG PREOP Viewer Interpretation Guideline Starting the viewer Launch the viewer for a patient by clicking this icon. A new browser tab or window will open. Data loading To ensure smooth switching between
More informationWebpage: Volume 3, Issue V, May 2015 eissn:
Morphological Image Processing of MRI Brain Tumor Images Using MATLAB Sarla Yadav 1, Parul Yadav 2 and Dinesh K. Atal 3 Department of Biomedical Engineering Deenbandhu Chhotu Ram University of Science
More informationECSE 626 Project Report Multimodality Image Registration by Maximization of Mutual Information
ECSE 626 Project Report Multimodality Image Registration by Maximization of Mutual Information Emmanuel Piuze McGill University Montreal, Qc, Canada. epiuze@cim.mcgill.ca Abstract In 1997, Maes et al.
More informationEPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing
Functional Connectivity Preprocessing Geometric distortion Head motion Geometric distortion Head motion EPI Data Are Acquired Serially EPI Data Are Acquired Serially descending 1 EPI Data Are Acquired
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 informationFunctional MRI in Clinical Research and Practice Preprocessing
Functional MRI in Clinical Research and Practice Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization
More informationIntroduction to Neuroimaging Janaina Mourao-Miranda
Introduction to Neuroimaging Janaina Mourao-Miranda Neuroimaging techniques have changed the way neuroscientists address questions about functional anatomy, especially in relation to behavior and clinical
More informationMultimodal Brain Warping Using the Demons Algorithm and Adaptative Intensity Corrections
Multimodal Brain Warping Using the Demons Algorithm and Adaptative Intensity Corrections Alexandre Guimond, Alexis Roche, Nicholas Ayache, Jean Meunier To cite this version: Alexandre Guimond, Alexis Roche,
More informationSPM8 for Basic and Clinical Investigators. Preprocessing. fmri Preprocessing
SPM8 for Basic and Clinical Investigators Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial
More informationSPM8 for Basic and Clinical Investigators. Preprocessing
SPM8 for Basic and Clinical Investigators Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial
More informationfmri pre-processing Juergen Dukart
fmri pre-processing Juergen Dukart Outline Why do we need pre-processing? fmri pre-processing Slice time correction Realignment Unwarping Coregistration Spatial normalisation Smoothing Overview fmri time-series
More informationMath background. 2D Geometric Transformations. Implicit representations. Explicit representations. Read: CS 4620 Lecture 6
Math background 2D Geometric Transformations CS 4620 Lecture 6 Read: Chapter 2: Miscellaneous Math Chapter 5: Linear Algebra Notation for sets, functions, mappings Linear transformations Matrices Matrix-vector
More informationAutomatic Brain Segmentation in Rhesus Monkeys
Automatic Brain Segmentation in Rhesus Monkeys Martin Styner a,b, Rebecca Knickmeyer b, Sarang Joshi c, Christopher Coe d, Sarah J Short d, John Gilmore b a Department of Psychiatry, University of North
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 informationMagnetic resonance image tissue classification using an automatic method
Yazdani et al. Diagnostic Pathology (2014) 9:207 DOI 10.1186/s13000-014-0207-7 METHODOLOGY Open Access Magnetic resonance image tissue classification using an automatic method Sepideh Yazdani 1, Rubiyah
More informationRIGID IMAGE REGISTRATION
RIGID IMAGE REGISTRATION Duygu Tosun-Turgut, Ph.D. Center for Imaging of Neurodegenerative Diseases Department of Radiology and Biomedical Imaging duygu.tosun@ucsf.edu What is registration? Image registration
More informationNon-rigid Image Registration
Overview Non-rigid Image Registration Introduction to image registration - he goal of image registration - Motivation for medical image registration - Classification of image registration - Nonrigid registration
More informationManual image registration in BrainVoyager QX Table of Contents
Manual image registration in BrainVoyager QX Table of Contents Manual image registration in BrainVoyager QX......1 Performing manual alignment for functional to anatomical images......2 Step 1: preparation......2
More informationForm follows func-on. Which one of them can fly? And why? Why would anyone bother about brain structure, when we can do func5onal imaging?
Why would anyone bother about brain structure, when we can do func5onal imaging? Form follows func-on Which one of them can fly? And why? h;p://animals.na5onalgeographic.com Why would anyone bother about
More informationWhite Matter Lesion Segmentation (WMLS) Manual
White Matter Lesion Segmentation (WMLS) Manual 1. Introduction White matter lesions (WMLs) are brain abnormalities that appear in different brain diseases, such as multiple sclerosis (MS), head injury,
More informationMRI Brain Image Segmentation based on Thresholding
International Journal of Advanced Computer Research (ISSN (print): 49-777 ISSN (online): 77-7970) Volume-3 Number- Issue-8 March-03 MRI Brain Image Segmentation based on Thresholding G. Evelin Sujji, Y.V.S.
More informationDeformable Registration Using Scale Space Keypoints
Deformable Registration Using Scale Space Keypoints Mehdi Moradi a, Purang Abolmaesoumi a,b and Parvin Mousavi a a School of Computing, Queen s University, Kingston, Ontario, Canada K7L 3N6; b Department
More informationProf. Noah Snavely CS Administrivia. A4 due on Friday (please sign up for demo slots)
Robust fitting Prof. Noah Snavely CS111 http://www.cs.cornell.edu/courses/cs111 Administrivia A due on Friday (please sign up for demo slots) A5 will be out soon Prelim is coming up, Tuesday, / Roadmap
More informationImage Processing. Image Features
Image Processing Image Features Preliminaries 2 What are Image Features? Anything. What they are used for? Some statements about image fragments (patches) recognition Search for similar patches matching
More informationRegistration D.A. Forsyth, UIUC
Registration D.A. Forsyth, UIUC Registration Place a geometric model in correspondence with an image could be 2D or 3D model up to some transformations possibly up to deformation Applications very important
More informationMulti-Atlas Brain MRI Segmentation with Multiway Cut
Multi-Atlas Brain MRI Segmentation with Multiway Cut Duygu Sarikaya, Liang Zhao, and Jason J. Corso SUNY Buffalo, Computer Science and Engineering Department, 338 Davis Hall- Buffalo, New York, USA 14260-2500
More informationAppendix E1. Supplementary Methods. MR Image Acquisition. MR Image Analysis
RSNA, 2015 10.1148/radiol.2015150532 Appendix E1 Supplementary Methods MR Image Acquisition By using a 1.5-T system (Avanto, Siemens Medical, Erlangen, Germany) under a program of regular maintenance (no
More informationGPU-Based Implementation of a Computational Model of Cerebral Cortex Folding
GPU-Based Implementation of a Computational Model of Cerebral Cortex Folding Jingxin Nie 1, Kaiming 1,2, Gang Li 1, Lei Guo 1, Tianming Liu 2 1 School of Automation, Northwestern Polytechnical University,
More informationBESA Research. CE certified software package for comprehensive, fast, and user-friendly analysis of EEG and MEG
BESA Research CE certified software package for comprehensive, fast, and user-friendly analysis of EEG and MEG BESA Research choose the best analysis tool for your EEG and MEG data BESA Research is the
More informationBasic fmri Design and Analysis. Preprocessing
Basic fmri Design and Analysis Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial filtering
More informationAutomatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge
Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge Christian Wasserthal 1, Karin Engel 1, Karsten Rink 1 und André Brechmann
More informationFree-Form B-spline Deformation Model for Groupwise Registration
Free-Form B-spline Deformation Model for Groupwise Registration The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Balci
More informationLilla Zöllei A.A. Martinos Center, MGH; Boston, MA
Lilla Zöllei lzollei@nmr.mgh.harvard.edu A.A. Martinos Center, MGH; Boston, MA Bruce Fischl Gheorghe Postelnicu Jean Augustinack Anastasia Yendiki Allison Stevens Kristen Huber Sita Kakonoori + the FreeSurfer
More information