Samenvatting Medische Beeldanalyse – 8DC00
Index
MEDICAL IMAGE REGISTRATION......................................................................................... 2
Introduction to image registration..................................................................................2
Geometrical transformations.......................................................................................... 2
Image transformation..................................................................................................... 6
Point-based registration.................................................................................................. 7
Evaluation of the registration accuracy...........................................................................9
Intensity-based similarity metrics.................................................................................10
SEGMENTAION OF MEDICAL IMAGES................................................................................14
Evaluation of segmentation.......................................................................................... 14
Segmentation in feature space.....................................................................................16
Segmentation in feature space – clustering..................................................................18
Segmentation in feature space – classification.............................................................18
Generalization............................................................................................................... 20
Overfitting..................................................................................................................... 20
Overfitting in kNN......................................................................................................... 21
Dimensionality reduction..............................................................................................21
Atlases.......................................................................................................................... 23
Active shapes models................................................................................................... 25
COMPUTER-AIDED DIAGNOSIS.........................................................................................27
Introduction to CAD; Linear regression.........................................................................27
Logistic regression and neural networks.......................................................................28
Performing machine learning experiments...................................................................30
CAD convolutional neural networks..............................................................................31
Page 1 out of 34
,MEDICAL IMAGE REGISTRATION
Introduction to image registration
Image registration: the determination of a geometrical transformation that aligns one
view of an object with another view of that object, or another object. This can be the case
because of:
- Different positioning of the patient
- Movements of organs
- Movements of patient
- Distortions caused by imaging system
- Changes caused by interventions (e.g. surgery, chemo) in between the acquisition
of the images.
Applications of image registration:
- Combining information from different sources
- Comparison: differences in (groups of) subjects, or monitoring changes in a single
subject
- Segmentation
- Motion correction
- Image-guided treatment
- Atlas, model of average anatomy.
Review of linear algebra
Matrix transpose:
Special matrices and vectors:
- Unit vector
- Symmetric matrix
- Orthogonal matrix
Determinant: the determinant of a transformation matrix T is the signed area of a unit
square shape after transforming it with T. The sign reflects whether the orientation has
changed.
Here no flip but rotation!
Geometrical transformations
Page 2 out of 34
,Translation:
For which the distance between two points (in 2D) can be described by the following
formula:
Rotation:
LET OP: Not every matrix can be considered as a rotation matrix. Rotation matrices must:
- Be orthogonal
- Have a determinant equal to 1
Page 3 out of 34
, Scaling
Shearing:
Reflection
Horizontal:
Vertical:
Composition of transformations: it is also possible to combine different sorts of
transformations. They can be combined by multiplying the transformation matrices.
- Rotation + translation (rigid) (links)
- Rotation, scaling + translation (rechts)
Page 4 out of 34
Index
MEDICAL IMAGE REGISTRATION......................................................................................... 2
Introduction to image registration..................................................................................2
Geometrical transformations.......................................................................................... 2
Image transformation..................................................................................................... 6
Point-based registration.................................................................................................. 7
Evaluation of the registration accuracy...........................................................................9
Intensity-based similarity metrics.................................................................................10
SEGMENTAION OF MEDICAL IMAGES................................................................................14
Evaluation of segmentation.......................................................................................... 14
Segmentation in feature space.....................................................................................16
Segmentation in feature space – clustering..................................................................18
Segmentation in feature space – classification.............................................................18
Generalization............................................................................................................... 20
Overfitting..................................................................................................................... 20
Overfitting in kNN......................................................................................................... 21
Dimensionality reduction..............................................................................................21
Atlases.......................................................................................................................... 23
Active shapes models................................................................................................... 25
COMPUTER-AIDED DIAGNOSIS.........................................................................................27
Introduction to CAD; Linear regression.........................................................................27
Logistic regression and neural networks.......................................................................28
Performing machine learning experiments...................................................................30
CAD convolutional neural networks..............................................................................31
Page 1 out of 34
,MEDICAL IMAGE REGISTRATION
Introduction to image registration
Image registration: the determination of a geometrical transformation that aligns one
view of an object with another view of that object, or another object. This can be the case
because of:
- Different positioning of the patient
- Movements of organs
- Movements of patient
- Distortions caused by imaging system
- Changes caused by interventions (e.g. surgery, chemo) in between the acquisition
of the images.
Applications of image registration:
- Combining information from different sources
- Comparison: differences in (groups of) subjects, or monitoring changes in a single
subject
- Segmentation
- Motion correction
- Image-guided treatment
- Atlas, model of average anatomy.
Review of linear algebra
Matrix transpose:
Special matrices and vectors:
- Unit vector
- Symmetric matrix
- Orthogonal matrix
Determinant: the determinant of a transformation matrix T is the signed area of a unit
square shape after transforming it with T. The sign reflects whether the orientation has
changed.
Here no flip but rotation!
Geometrical transformations
Page 2 out of 34
,Translation:
For which the distance between two points (in 2D) can be described by the following
formula:
Rotation:
LET OP: Not every matrix can be considered as a rotation matrix. Rotation matrices must:
- Be orthogonal
- Have a determinant equal to 1
Page 3 out of 34
, Scaling
Shearing:
Reflection
Horizontal:
Vertical:
Composition of transformations: it is also possible to combine different sorts of
transformations. They can be combined by multiplying the transformation matrices.
- Rotation + translation (rigid) (links)
- Rotation, scaling + translation (rechts)
Page 4 out of 34
