Medical Image Processing: Feature Recognition and Classification
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Questions and Answers

What is the simplest chain code mechanism also known as?

  • Crack code (correct)
  • Translation invariant
  • Boundary descriptor
  • Scale invariant
  • What is the purpose of allocating numbers based on directions in chain code?

  • To preserve the information of interest
  • To provide good compression of boundary description
  • To make the chain code rotation invariant
  • To reduce the boundary of an object to a sequence of numbers (correct)
  • What is the first step in constructing a chain code?

  • Stop if the next point is the initial point or the end of the boundary
  • Re-sample the image to a lower resolution
  • Select some starting point of the boundary and represent it by its absolute coordinates (correct)
  • Represent every consecutive point by a chain code
  • What is an advantage of chain code?

    <p>It provides good compression of boundary description</p> Signup and view all the answers

    What is a problem with chain code?

    <p>It has long chains of codes</p> Signup and view all the answers

    What is a solution to the problem of chain code being sensitive to noise?

    <p>Re-sample the image to a lower resolution</p> Signup and view all the answers

    What is a limitation of chain code?

    <p>It has no invariance to Rotation and Scale</p> Signup and view all the answers

    What is a problem with chain code sequence?

    <p>It depends on a starting point</p> Signup and view all the answers

    What do the numbers in the chain code represent?

    <p>Directions followed by the bug tracking algorithm</p> Signup and view all the answers

    What is the purpose of the bug tracking algorithm in chain code?

    <p>To allocate numbers based on directions</p> Signup and view all the answers

    Study Notes

    Feature Recognition and Classification

    • Feature recognition is necessary to reduce the dimensionality of the classification task by measuring essential properties or features of objects.

    Features

    • Features are higher-level representations of structure and shape.
    • Features should be chosen to preserve the information that is important to the particular task at hand.
    • Two categories of features:
      • Describing the contents of objects (e.g., mean pixel value, standard deviation, contrast, entropy, texture)
      • Describing the shape of objects (e.g., size, perimeter, circularity, skeleton, Euler number)

    Image Representation and Feature Extraction

    • Representation makes object information more accessible for computer-interpretation.
    • Description quantifies the representation of the object.
    • Feature extraction detects and represents features of interest within an image for further processing.

    Why Features?

    • Features are used for diagnosis, radiation therapy, and multi-modality registration of images.
    • Features are extracted to identify treatment areas and boundaries.

    Features Characteristics

    • Features should be:
      • Robust (invariant to translation, orientation, scale, and illumination)
      • Discriminating (different values for objects in different classes)
      • Reliable (similar values for objects of the same class)
      • Independent (uncorrelated features)
      • Simple to extract for large-scale screening.

    Feature Vector

    • A feature vector or pattern vector contains the measured features for a particular object or region.
    • 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.

    Types of Representation

    • Two types of representation:
      • Boundary (External characteristics)
      • Region (Internal characteristics)

    Boundary Descriptors Techniques

    • Chain Code:
      • A boundary representation technique that represents a contour as a sequence of straight line segments of specified length and direction.
      • The simplest chain code mechanism allocates numbers based on directions, reducing the boundary of an object to a sequence of numbers.
      • Chain code provides good compression of boundary description but is sensitive to noise and not rotation and scale invariant.

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    Quiz Team

    Description

    This quiz covers the concepts of feature recognition and classification in medical imaging, including pre-processing and dimensionality reduction. It is based on Lecture 6 of a medical imaging course.

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