Medical Imaging Lecture 6

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36 Questions

What is the purpose of feature extraction and representation techniques?

To convert segmented objects into representations of their main features and attributes

What is used to quantify the representation of an object?

Description

What is the circularity of an object calculated as?

4πA/P2

What is the purpose of calculating the Euler number of an image?

To find the number of connected components minus the number of holes in the image

What is feature extraction?

The process of detecting and representing features of interest within an image

What is the purpose of using features in diagnosis?

To classify features into specific classes

What can be obtained from the skeleton or medial axis transform of an object?

Points within the skeleton, such as branch points and end points

What is representation in image processing?

The process of making object information more accessible for computer-interpretation

What is the primary goal of feature recognition in medical imaging?

To reduce the dimensionality of the classification task

What type of features describe the contents of the objects in medical imaging?

Features obtainable from the histogram of an object using region-of-interest processing

What is the benefit of using features in medical imaging?

To preserve the information that is important to the particular task at hand

What is the purpose of region-of-interest processing in medical imaging?

To extract features describing the contents of the objects

What is the name of the method used to describe the texture of an object in medical imaging?

Statistical moments of the gray-level histogram

What is the result of not reducing the dimensionality of the classification task in medical imaging?

The classification task becomes more complex

What does the crack code mechanism assign to the direction followed by a bug tracking algorithm?

A number

What is the result of allocating numbers based on directions in the boundary of an object?

A reduced sequence of numbers

What is the first step in constructing chain codes?

Select a starting point of the boundary

What is a limitation of chain codes?

They are not rotation invariant

What is an advantage of chain codes?

They preserve the information of interest

What is a solution to the problems of chain codes?

Re-sample the image to a lower resolution

What is a problem of chain codes?

They are too long

Why is a starting point important in chain codes?

The chain code sequence depends on it

What is the purpose of redefining the starting point in a chain code?

To make the sequence of numbers form an integer of minimum magnitude

What does the first difference of a chain code represent?

The number of direction changes between 2 adjacent elements

What is the shape number invariant to?

Rotation and starting point

What is the advantage of using the shape number in boundary descriptors?

It is rotation invariant and insensitive to the starting point

What is the purpose of the chain code in boundary descriptors?

To represent the direction changes in a contour

What is the relationship between the chain code and the first difference?

The chain code is used to compute the first difference

What is the primary purpose of multi-modality registration of images?

To recognize correspondence between equivalent structures

What is the characteristic of robust features?

They are normally invariant to translation, orientation, scale and illumination

What is the purpose of a feature vector?

To contain the measured features for a particular object or region

What is the goal of classification in feature space?

To assign each feature vector to a set of classes

What is the importance of feature independence?

It prevents the consideration of correlated features as separate features

Why should features be simple to extract for screening?

To perform on large numbers of patients

What is the characteristic of discriminating features?

The range of values for objects in different classes should be different and preferably well-separated and non-overlapping

What is the dimensionality of the feature space for n features?

n-dimensional

Study Notes

Feature Recognition and Classification

  • Feature recognition and classification are necessary to reduce the dimensionality of the classification task by measuring essential properties or features of the objects.
  • Features are higher-level representations of structure and shape, and should be chosen to preserve the information that is important to the particular task at hand.

Features

  • Examples of features include those describing the contents of the objects and those describing their shape.
  • Features describing the contents of the objects include:
    • Features obtainable from the histogram of an object using region-of-interest processing, such as:
      • Mean pixel value (grayness or color) and its standard deviation
      • Contrast and entropy
      • Texture of an object, using statistical moments of the gray-level histogram of the object or its fractal dimension
  • Features describing the shape of objects include:
    • Size or area (A) of an object, obtained directly from the number of pixels comprising each object
    • Perimeter (P)
    • Circularity (4πA/P²)
    • Skeleton or medial axis transform or points within it, such as branch points and end points
    • Euler number: the number of connected components (i.e. objects) minus the number of holes in the image
    • Statistical moments of the boundary or area

Image Representation and Feature Extraction

  • Representation means making object information more accessible for computer-interpretation
  • Description means quantifying the representation of the object
  • Feature extraction is the process by which certain features of interest within an image are detected and represented for further processing
  • The common goal of feature extraction and representation techniques is to convert the segmented objects into representations that better describe their main features and attributes

Why Features?

  • Features are used for:
    • Diagnosis, involving classifying features into specific classes
    • Radiation therapy, extracting features to identify treatment areas and boundaries
    • Multi-modality registration of images, extracting and comparing features from each modality in order to recognize correspondence between equivalent structures

Features Characteristics

  • The choice of appropriate features depends on the particular image and application at hand
  • Features should be:
    • Robust (invariant to translation, orientation, scale, and illumination)
    • Discriminating (the range of values for objects in different classes should be different and preferably well-separated and non-overlapping)
    • Reliable (all objects of the same class should have similar values)
    • Independent (uncorrelated)

Feature Vector

  • A feature vector or pattern vector is a vector containing the measured features
  • For n features, the feature space is n-dimensional, with each feature constituting a dimension
  • In classification, the goal is to assign each feature vector to one of a set of classes

Boundary Descriptors Techniques

  • Chain Code:
    • Assigns a number to the direction followed by a bug tracking algorithm
    • By allocating numbers based on directions, the boundary of an object is reduced to a sequence of numbers
  • Chain Code Advantages:
    • Preserves the information of interest
    • Provides good compression of boundary description
    • Translation invariant
  • Chain Code Problems:
    • Long chains of codes
    • No invariance to Rotation and Scale
    • Sensitive to Noise
  • Solution:
    • Re-sample the image to a lower resolution before calculating the code

Boundary Descriptors Techniques (continued)

  • Chain Code Problem:
    • A chain code sequence depends on a starting point
  • Solution:
    • Treat a chain code as a circular sequence and redefine the starting point so that the resulting sequence of numbers forms an integer of minimum magnitude after circular shift
    • The first difference of a chain code is counting the number of direction changes (in counter-clockwise) between 2 adjacent elements of the code
  • Chain Code – first difference – shape number:
    • The shape number is Rotation invariant and Insensitive to the starting point used to compute the original sequence

This quiz covers feature recognition and classification in medical imaging, including pre-processing and dimensionality reduction. It is based on the lecture notes by Dr. Hossam Mahmoud Moftah and adapted from Geoff Dougherty's book on Image Processing for Medical Applications.

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