segmentation n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Segmentation PowerPoint Presentation
Download Presentation
Segmentation

Loading in 2 Seconds...

play fullscreen
1 / 7

Segmentation - PowerPoint PPT Presentation


  • 69 Views
  • Uploaded on

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. 1. Definition.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Segmentation' - onofre


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
segmentation
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.

1 definition
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”.

manual segmentaion
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
pixel based approaches
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
edge based approaches
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?
discrete dynamic contour
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…