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Glossary

You may find that in this course we use terminology that you are unfamiliar with. This is why we are creating this comprehensive glossary of terms.

Below you find short definitions for recurring terms in the course. The terms are ordered alphabetically. This glossary is a document on which you can collaborate by using the comment function in this step.

In case you feel you can phrase a definition for one of the terms below, please post your definition as a comment. The course educators will regularly monitor the comment section and add the best solutions to the glossary. In case there is an additional term for which you would like a definition, feel free to suggest them too. Please ‘like’ the comments you find correct and most useful.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

Active shape model (ASM)

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Introduced in:

Further reading:

C

Conditional distribution

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Introduced in:

Further reading:

Confidence region

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Introduced in:

Further reading:

Correlation

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Introduced in:

Further reading:

Correspondence

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Further reading:

Covariance function -

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Introduced in:

Further reading:

Covariance matrix

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Introduced in:

Further reading:

D

Deformation field

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Introduced in:

F

Fitting a model

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Further reading:

Free-form deformation

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Introduced in:

G

Gaussian distribution

See Normal distribution

Gaussian Process (GP)

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Introduced in:

Further reading:

Gaussian Process regression

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I

Intensity model

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Iterative Closest Point (ICP) algorithm

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Introduced in:

Further reading:

J

Joint distribution

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Further reading:

K

Karhunen-Loève expansion

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Introduced in:

Further reading:

Kernel function

M

Marginal distribution

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Introduced in:

Further reading:

Marginalisation property

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Introduced in:

Further reading:

Multivariate normal distribution

See Normal distribution

N

Normal distribution

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Further reading:

P

Point Distribution Model

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Introduced in:

Posterior model

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Further reading:

Principal Component Analysis (PCA)

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Prior model

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Introduced in:

Procrustes Alignment

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Further reading:

Positive (semi) definiteness

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Introduced in:

Further reading:

R

Registration

Rigid registration is the process of finding the best translation and rotation between two geometric objects. It is a process of minimizing the dissimilarity measure between the two geometric objects. The transformation matrix, which models the translation and rotation, will be applied to the geometric object being registered. Size can be filtered out if required. In this case, the registration would be called a similarity registration.

Non-rigid registration has the same goal as rigid registration, but seeks to find more general non-rigid transformations between the geometric objects.

Introduced in:

Further reading:

Regression

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Rigid transformation

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S

Sampling

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Further reading:

Shape

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Introduced in:

Shape family

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Introduced in:

Statistical shape model

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Introduced in:

Further reading:

This article is from the free online course:

Statistical Shape Modelling: Computing the Human Anatomy

University of Basel