1 / 15

Author: Gábor Bernát

Regional image segmentation using B-Spline level-set function. Author: Gábor Bernát. Scientific advisor : d r. ing. Szilágyi László. The Problem. What do you think when you see the following images:. Image Segmentation with problems like:. The segmentation itself

yana
Download Presentation

Author: Gábor Bernát

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Regional image segmentation using B-Spline level-set function Author: Gábor Bernát Scientific advisor: dr. ing. Szilágyi László

  2. The Problem • What do you think when you see the following images: • Image Segmentation with problems like: • The segmentation itself • Multiple independent segments • High noise levels • Low contrast • Scalability (rigidity of the contour)

  3. The Solution Image Processing - Segmentation Signal Processing Variational Models Active Contours Osher-Sethian Level-Set Framework + topologically flexible Chan-Vese Regional Segmentation + can handle noise + low contrast Michael Unser B-Splines as Filters + scalability + easy implementation Olivier Bernard: Variational B-Spline Level Set IEEE Transactions On Image Processing, June 2009

  4. How it works? • The Level-Set Function: • The evolution of the level-set:

  5. Brief Algorithm Break Down

  6. Implementation • Object-oriented programming – C++ • Enclose functions and variables into a class: BSplineLevelSet • Simplify the usage for the user

  7. Usage • From an application:

  8. Graphical User Interface

  9. Performance • Intel 2.53 GHz CPU + 4GB RAM • Leaf Oliver Bernard (2009) – Intel 1.4 GHz CPU + 1 GB RAM • Scale 4 – 2.03 second

  10. Key milestone check #1 • Results: • Run-time sneak peek:

  11. Key milestone check #2

  12. Other areas where we can use it

  13. To where from here? • Implementation on GPU • Improve level-set minimization time • Adapt it to various medical image problems • Tracking the left ventricle in an echocardiographic videos • Segmentation of teeth in computer tomography images

  14. Questions ??????????????????

  15. Thank you for your attention!

More Related