Image Processing and MRI Fundamentals

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Questions and Answers

What type of measurements does MR (magnetic resonance) primarily assess?

Water content (correct)

Bone density

X-ray absorption

Temperature variations

Which application involves combining multiple images to create a cohesive view?

Image mosaics (correct)

Change detection

3D modeling

Industrial inspection

What is one of the primary requirements for creating image mosaics?

Wide camera viewpoints

High-resolution images

Oblique camera angles

Simple surface geometry (correct)

What is the role of range scanners in building 3D models?

They store (x,y,z) measurements at each pixel location. Signup and view all the answers

What aspect of changes detected between images poses a challenge in image registration?

Deciding if the changes are significant. Signup and view all the answers

Which interpolation method uses the nearest neighboring pixel values for determining output pixel values?

Nearest Neighbor Signup and view all the answers

Which optimization method is specifically designed for nonlinear optimization problems?

Powell’s Direction Set method Signup and view all the answers

What does the bag-of-words model primarily focus on in image classification?

Representation of visual features Signup and view all the answers

What is a major limitation of simple template matching in object recognition?

It fails in three-dimensional scene analysis. Signup and view all the answers

In learning-based vision, what do terms like 'semantic vision' refer to?

Understanding the context and meaning of a visual scene Signup and view all the answers

What does the variable 'q' represent in the image registration problem?

Homologous pixel location in the second image Signup and view all the answers

What are the unknowns that the registration problem faces?

Correspondences, mapping function form, and mapping parameters Signup and view all the answers

In the context of multimodal integration for image registration, what influences the need for registration?

Variations in image dimensions and information provided in each image Signup and view all the answers

What does the mapping function T(p;θ) refer to in image registration?

The spatial transformations to identify pixel correspondences Signup and view all the answers

Which of the following scenarios does NOT typically require image registration?

Images taken by sensors with coincident modalities Signup and view all the answers

What is the purpose of Normalized Mutual Information (NMI) in image analysis?

To measure sensitivity of mutual information to changes in image overlap Signup and view all the answers

In the context of geometry transformation, which aspect is not specified for the images acquired from different modalities?

Contrast enhancement Signup and view all the answers

Which of the following correctly expresses the image coordinate transformation?

T(x, y, z) = (x', y', z') = (a00 a01 a02 0)(x) + (a10 a11 a12 0)(y) + (a20 a21 a22 0)(z) Signup and view all the answers

What is not a feature that differs between images from different modalities?

Image quality Signup and view all the answers

Which statement regarding the transformation of target and template images is incorrect?

Both images maintain the same physical coordinates. Signup and view all the answers

Study Notes

AIS412 Lecture 7: Image Registration

Lecture material sourced from Carnegie Mellon University's Computer Vision course and Stanford University's CNN for Visual Recognition course.
Image registration involves finding the mapping function (T) to align homologous pixels in two images.
A pixel in the first image (p) is mapped to a homologous pixel in the second image (q) using a transformation function (T(p; θ), where θ represents the unknown parameters of the mapping).
Example mapping function:
- T(p;θ) = (sx + tx, sy + ty) where s = scaling factor, tx = horizontal translation, and ty = vertical translation
Problems in image registration:
- Determining the mapping function (T)
- Identifying unknown mapping parameters (θ)
- Defining correspondences between pixels in the two images (p, q)

Applications

Multimodal Integration:
- Used when two or more sensors view the same region/volume.
- Different sensors (e.g., range images vs. intensity images or CT volumes vs. fluoro images) provide different information about the same subject, requiring registration.
- Some sensors provide redundant information from the same modality, so registration is not necessary.
Example: MR-CT Brain Registration:
- MRI measures water content; CT measures x-ray absorption.
- Bone appears brightest in CT, darkest in MR.
- Both images are 3D volumes.
Image Mosaics:
- Used for many overlapping images to construct a complete view as a mosaic (e.g., stitch multiple images together).
- Requires limited camera viewpoint and simple geometric constraints (e.g., rotation about the optical center of simple geometry like a plane or quadratic surface).
Building 3D Models:
- Range scanners produce a set of 3D measurements at each pixel location.
- Necessary to register multiple range images and texture map to create a 3D model.
- Common application in reverse engineering and digital architecture/archaeology.
Change Detection:
- Used when images from different times are compared.
- Registration allows differences between images to be identified, potentially indicative of change.
- Sometimes difficulties when determining whether change exists.
Multi-subject Registration/Object Recognition/Industrial Inspection:
- Multi-subject registration creates organ variation atlases, used to detect abnormal variations.
- Object recognition involves aligning an object model with an image of an unknown object. Industrial inspection involves comparing a CAD model to an instance of a part.

Steps Toward Image Registration/Problem Definition

Analyse the images
Define the appropriate image primitives (geometric and intensity)
Determine the transformation models
Design an initialisation technique
Develop constraints and error metrics on transformation estimates
Design a minimization algorithm
Develop a convergence criteria

Pervasive Questions:

Identify intensity information or image structures in a given set of images that might be consistent between the images.
Analyse the geometric relationship between the image coordinate systems.
Establish prior information that confines the possible transformation domains.

Spatial Transformations

Rigid: Preserves lengths and angles (translation, rotation).
Affine: Preserves parallel lines, but not lengths and angles (scaling, shear, rotation, translation)
Projective: Doesn't preserve parallelism, lengths or angles
Perspective: A specific type of projective transformation related to planar homography
Global Polynomial (Spline): used for complex transformations

Similarity Metrics in Registration

Absolute difference: Simple measure of difference between corresponding pixel values in images.
SSD (Sum of Squared Differences): Calculates the sum of squared differences between corresponding pixel values in images, providing a more comprehensive comparison than absolute difference.
Correlation Coefficient: Employs the correlation between corresponding pixel values in images to provide a measure of similarity/alignment.
Mutual Information/Normalized Mutual Information: Used to determine the relationship between corresponding pixel intensity values in images.

Search Strategy

Powell's direction set method, downhill simplex method, dynamic programming, relaxation matching, hierarchical techniques

Multi-modality Brain Image Registration

Intensity-based 3D/3D rigid transformation (6 degrees of freedom).
Maximisation of Normalized Mutual Information.
Simplex downhill/Multi-resolution

Mutual Information as a Similarity Measure

Mutual information is used to quantify statistical dependence between corresponding voxel intensities in aligned images.

Normalized Mutual Information

Extends mutual information to account for sensitivity to overlap changes

Image Coordinate Transformations

Image features (dimension, voxel size, slice spacing, gantry tilt, orientation) may differ due to acquisition from distinct modalities.
Coordinate transforms are used to convert voxel units (column, row, slice spacing) to millimeter units, with the origin centred within the volume of the image.

Target Image & Template Image

Different images for comparison/analysis; images to be transformed is termed the "target" or "moving" image while the second image is the "template" or "fixed" image.

Interpolation Methods

Nearest Neighbor, Tri-linear, Partial-Volume, Higher-order partial-volume interpolation.

Optimization Methods used in Image Registration

Powell's Direction Set method/Downhill Simplex method.

Further topics in the lectures (from other pages):

Bag-of-Words (BoW) for image classification (a technique that learns representations from visual features).
Types of Challenges for Image registration: Variable Viewpoint, Variable Illumination, Scale, Deformation, Occlusion, Background Clutter, Intra-class Variations.
Feature extraction techniques: Regular grid, Interest point detectors/Other methods (SIFT).
Methods for Learning Dictionaries (K-means clustering).

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Description

This quiz covers key concepts in image processing and magnetic resonance imaging (MRI). It explores topics ranging from image registration challenges to the bag-of-words model in classification. Test your knowledge on optimization methods and 3D modeling techniques.