Image Processing

Module aims

 In this module you will:

 -be introduced to digital image processing relevant methods for image analysis.

-develop an appreciation of aspects of computation in interpreting or “parsing” images

-be introduced to some of the biomedical, clinical and research applications of image processing and computer vision.

Learning outcomes

Upon successful completion of this module you will be able to:

  • Describe the key concepts in image analysis including image types, representations & basic operations & transformations. 
  • Discuss common topics relevant to computer vision including potential applications of image processing and computer vision (medical & non-medical) and the relationships between mammalian visual systems and standard computational approaches to vision.
  • Calculate linear image transforms numerically.
  • Apply effectively moment calculations and the Hessian matrix to capture information about spatial structure.
  • Apply effectively a range of neighbourhood image operators including spatial convolution using mask design where needed.
  • Distinguish where image segmentation is necessary and describe image segmentation approaches and applications.
  • Write code for quantitative image processing making use of common image processing tools in Matlab
  • Systematically identify, label and quantify spatially localised structures using semi-automated methods

Module syllabus

In this module you will cover the following topics: 

1. Basic Concepts: introductory topics and biological vision

2. Binary Images and how these can be processed

3. Image Transforms

4. Neighbourhood Operators

5. Image Segmentation

6. Image Registration

7. Data Driven Approaches

Pre-requisites

 Calculus, derivatives, Linear Algebra. Eigenvalues and eigenvectors of a matrix. Knowledge of random variables would also be useful.

Teaching methods

Lectures: 20 hours

Labs: 18 hours

Assessments

 Examinations:

●  Written exam:  80% weighting

Courseworks:

●  Quiz: 20% weighting;

1 quiz, which will be on the last week of instruction, during the lab session