1. Segmentation OBJECTIVES 1. Define segmentation 2. Example 3. Brief discussion of-manual segmentation-pixel-based approaches-edge-based approaches 4. Demonstrate discrete dynamic contour Major part of ECE9202 so only a preview is offered here.

2. 1. Definition Segmentation: Partitioning an image into regions. From a practical perspective, the regions would correspond to objects of interest. Cross-sectional image through prostate. The prostate is the dark “blob”.

3. Example

4. Manual segmentaion • Have user manually outline boundary of object using a paint program (roipoly function in MATLAB) • Accuracy depends on experience of observer, fatigue, skill with using mouse, etc • Potential for variability in results • Intra-observer variability: same observer gets different results on different occasions • Inter-observer variability: different observers disagree with each other

5. Pixel-Based Approaches • Thresholding • Why will it not work for our example? • Region growing – user selects pixel inside object and adjacent pixels are added that meet certain criteria (mainly gray-level statistics) • Why better? • Will it work here? • Classification – for each pixel in image, compute some features and then classify as part of object 1, 2,… or background

6. Edge-based approaches • Simplest approach is to find edges in image (e.g., using edge function in MATLAB) and link edges • Will this work for our example?

7. Discrete Dynamic Contour • Demonstration of Discrete Dynamic Contour • Form of edge linking • Advantages over previous approach (see previous slide): • Forces contour to be continuous when linking edge information • User can control smoothness • Advanced topics: incorporation on shape contraints, user-enforced constraints, region information…