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Research Topics in Computer Science and Medical Imaging. Gordon Devoe - 2014. A Nonlocal Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images. Lili He & Ian Greenshields February 2009. Magnetic Resonance (MR) Images. - PowerPoint PPT Presentation
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Research Topics in Computer Science and Medical Imaging
Gordon Devoe - 2014
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A Nonlocal Maximum Likelihood Estimation Method for Rician Noise
Reduction in MR Images
Lili He & Ian GreenshieldsFebruary 2009
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Magnetic Resonance (MR) Images MRI is the newest most versatile medical imaging
technique available (very minor fluctuations in chemical composition can be determined).
High resolution images of a patient’s interior body without surgery.
Strong magnets and pulses of radio waves used to manipulate the natural magnetic properties in the body.
Especially useful for capturing images of the brain, spine, organs and soft tissue.
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Magnetic Resonance (MR) Images
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Noise in Medical Resonance Images
Sources Thermal noise from the conductivity of the
system’s hardware. Inductive losses from the conductivity of the
object being images. Factor Trade-offs
Resolution Signal-to-noise ratio (SNR) Acquisition speed
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Denoising MR Images
By denoising magnetic resonance (MR) images we can improve medical processes including: Clinical Diagnosis
the determination of the nature of a disease distinguishing one disease from another.
Tissue Classification Systematic arrangement of similar tissues on the
basis of certain differing characteristics. Image Segmentation
Coming Soon…
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Research Topics In Medical Imaging Image Registration Image Segmentation
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Image Registration Methods: A Survey
Zitová, Barbara and Flusser, Jan. Image and Vision Computing, ISSN 0262-8856, 2003,
Volume 21, Issue 11, pp. 977 - 1000
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Image Registration Image registration is one of the most fundamental
concepts when using computer science techniques to compare images with one another.
Image registration is the process of overlaying two or more pictures/images of the same scene and comparing them from different viewpoints, times and/or by different sensors.
The intersection of these images involves a set of changes influenced by different imaging conditions or various data sources such as image fusion, change detection and multi-channel image restoration.
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Image Registration - Applications Multispectral Classification Environmental Monitoring Change Detection Image Mosaicking Weather Forecasting Creating super-resolution images Geographic information systems (GIS) Cartography Computer Vision
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Image Registration – Applications Medical Comparing nuclear magnetic resonance spectra data
(NRM) with computer tomography (CT) data. Monitoring tumor growth Comparing patient data with anatomical atlases
(digital libraries of anatomy information). Brain mapping.
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Image Registration – 3 Types Multi-Temporal Image Registration
Images captured at different times/conditions Evaluate changes in the scene Tumor evolution monitoring Healing therapy
Multi-Modal Image Registration Images from same scene are captures with sensors
with different characteristics. Integrate the information obtained from different
source streams to gain more detailed scene representation.
Combine MRI, ultrasound or CT, magnetic resonance spectroscopy for radiotherapy & nuclear medicine applications.
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Image Registration – 3 Types Scene-to-model Image Registration
Images of a scene and models are compared. Models are computer generated and images are
overlaid on the computer model. Comparing a patient’s image with a digital model.
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Image Registration - Steps Feature Detection
Salient and distinctive objects (close-boundary regions, edges, contours, edges, lines, corners, etc.)
Features are then represented by their controls points.
Feature Matching Correspondences between the features detected in
the sensed and reference image are established. Transform Model Estimation
Type and parameters of mapping-functions aligning the sensed/reference image are established.
Image Resampling and transformation Sensed image is transformed based on the
established mapping functions
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Image Registration - Steps
Feature Detection
Feature Matching
Transform Model Estimation
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Image Registration – Feature Detection Features
Salient structures Regions (forests, lakes, fields) Lines (region boundaries, coastlines, roads, rivers) Points (region corners, lines intersections, points on
curves) Stable in time and stay in fixed position Distinct and spread all over the image.
Features - Medical Domain Line detection to compare anatomical structures. Interactive selection. Introduction of extrinsic features (screw markers,
dental adapters, etc.)
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Image Registration – Feature Matching Sensed and references images can be compared using
Intensity values Feature Spatial Distributions Feature Symbolic Descriptions
Feature Mapping can be divided in to two main categories: Area-based Feature Detection Feature Based Feature Detection
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Image Registration – Area Based Correlation “Template Matching”
Image intensities are directly matched without any structural analysis.
Sensitive to noise, varying illumination, and/or by using different sensor types.
Useful for real-time applications and easy to implement hardware.
figure, imshowpair(peppers(:,:,1),recovered_onion,'blend')
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Image Registration – Area Based Mutual Information
Statistically dependency between two data sets. Comparing anatomical and sensed images of
patient’s body.MI criterion (bottom) computed in the neighborhood of point P. Maximum of MI shows correct matching position (point A). Point B indicates the false matching position selected by human operator.
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Image Registration – Transform Model Est Once features have been discovered in the
sensed/referenced images a mapping function can be constructed to overlay the sensed image onto the reference image.
The mapping function is designed in a way to ensure the correspondence of the control points from the sensed/reference images are as close as possible.
Global mapping models use all control points for estimating one set of mapping function parameters for the entire image.
Local mapping models break the images in to sections making the function parameters depend on the location of their support in the image and the mapping function is defined for each section separately.
Changes in medical images tend to appear locally.
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Image Registration – Transform Model Est
Similarity Transform (top left) Affine Transform (top right) Perspective Projection (bottom left) Elastic Transform (bottom right)
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Current Methods in Medical Image Segmentation
Dzung L. Pham, Chenyang Xu, Jerry L. PrinceJohn Hopkins University
Department of Electrical & Computer Engineering
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Image SegmentationWhat is image segmentation?
Images are divided up in to smaller pieces or groups of pixels.
Segments are used to simplify or change the original image to a format easier to analyze.
Used to identify objects, lines, curves, etc. within an image.
Non-Trivial problem - noise, missing data, overlap of features, etc.
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Image Segmentation - MedicalIn the medical domain image segmentation is extremely important when using computers to facilitate the growing number of images in biomedical-imaging applications.
Quantify tissue volumes. Reconstruction of soft tissues such as the cerebral
cortex. Study of anatomical structures. Treatment planning Computer-integrated surgery Growing number of use cases…
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Image Segmentation - MethodsImage segmentation can be roughly divided in to eight categories:
Thresholding Approaches Region Growing Approaches Classifiers Clustering Approaches Markov Random Fields Models (extension) Artificial Neural Networks Atlas-Guided Approaches
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Image Segmentation - ThresholdingImage segmentation can be achieved by assigning an intensity value to every pixel of an image. Pixels can then be categorized based on whether or not they meet this threshold value.
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Image Segmentation - Thresholding Medical Uses
Digital Mammography Limitations
Only two classes are generated (cancerous/healthy).
Cannot be applied to multichannel images. Doesn’t take in to account spatial characteristics. Sensitive to noise and intensity inhomogeneities
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Image Segmentation – Region GrowingA ‘seed point’ is used to extract pixels from a particular part of the image and then transformations are made to the seed to perform further analysis. Separate regions that have the same properties. Provide original images with good segmentation
results. Choose multiple criteria at the same time.
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Image Segmentation – Region Growing
Original Figure 155 ~ 255
190 ~ 255 255 ~ 255
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Image Segmentation - ClusteringImage segmentation using clustering algorithms is very similar to segmentation classification methods except clustering algorithms do not require any training data.
The K-means algorithm iteratively computes a mean intensity for each class and classifies each pixel of the image to the class with the closest mean.
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Image Segmentation - Clustering Advantages
Do not require training data (self-learning). Lack of spatial modeling provides fast
computation. Disadvantages
Sensitive to noise and intensity inhomogeneities. Do not directly support spatial modeling.
Potential Algorithms K-means Algorithm Fuzzy c-means Algorithm Expectation-maximization Algorithm
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Image Segmentation – MRF Models Statistical model that can be used within segmentation
methods such as clustering. Models spatial interaction between nearby pixels.
K-means Segmentation With MRF
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Image Segmentation – MRF ModelsMedical images are a good candidate for Markov Random Field (MRF) models because pixels are generally in the same area as others from the same class and are widely used in digital mammograms.
• A depends on B and D• B depends on D and A• C Depends on E• Etc.
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Image Segmentation – Deformable Models Deformable models delineate region boundaries by
using closed parametric curves or surfaces. These curves and surfaces deform when under the
influence of internal or external forces
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Image Segmentation – Deformable ModelsAdvantages
Ability to directly generate closed parametric curves or surfaces from images.
Incorporation of a smoothness constraint that provides robustness to noise and spurious edges.
Disadvantages
Manual interaction is required to place initial model and choose appropriate parameters.
Deformable models exhibit poor convergence to concave boundaries.
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Image Segmentation – Deformable Models
Deformable surface used for the reconstruction of the cerebral cortex.
Intersection between deformable surface and orthogonal slices of the MR image.
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Image Segmentation – Atlas Guided When an atlas or template is available atlas-guided
image segmentation methods can be employed to anatomically separate features.
A template can simply be a collection of information about the anatomy of an image being segmented.
A Template can continually be re-used to segment other images of the same type.
By using an original image and the pre-segmented template a ‘warped’ image can be generated by using linear and non-linear transformations.
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Image Segmentation – Atlas Guided
Template Image Target Image Warped Template
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Image Segmentation – Atlas Guided
Three slices from a MR brain volume image overlaid with the atlas.
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Image Segmentation – Future Work Future work
Improving accuracy. Incorporating prior information from atlases.
Increasing precision tolerances. Combine discrete & continuous spatial-domain
segmentation methods. Improve computational speed of segmentation
methods. Multiscale processing Parallelizable methods
Reduce amount of manual interaction.
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Questions
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ReferencesMedical Image Registration
Image registration methods: a survey by Zitová, Barbara and Flusser, Jan. Image and Vision Computing, ISSN 0262-8856, 2003, Volume 21, Issue 11, pp. 977 – 1000
Q. Chen, M. Defrise, F. Deconinck, Symmetric phase-only matched filtering of Fourier–Mellin transform for image registration and recognition, IEEE Transactions on Pattern Analysis and Machine Intellingence 16 (1994) 1156–1168.
Multimodality image registration by maximization of mutual information by Maes, F; Collignon, A; Vandermeulen, D; Marchal, G; Suetens, P. IEEE transactions on medical imaging, ISSN 0278-0062, 04/1997, Volume 16, Issue 2, pp. 187 – 198
Medical Image Segmentation
Current methods in medical image segmentation by Pham, D L; Xu, C; Prince, J L Annual review of biomedical engineering, ISSN 1523-9829, 2000, Volume 2, p. 315
http://en.wikipedia.org/wiki/Image_segmentationhttp://en.wikipedia.org/wiki/Region_growinghttp://en.wikipedia.org/wiki/Markov_random_field
Magnetic Resonance Imaging (MRI) Noise Reduction A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images by He, Lili and Greenshields, Ian R IEEE transactions on medical imaging, ISSN 0278-0062, 02/2009, Volume 28, Issue 2, pp. 165 - 172