1 / 21

Probability Software Solution for Medical Segmentation

Explore applied probability concepts to develop software for medical image segmentation. Learn random variables, PDF, Gaussian distributions, and Bayes' rule. Utilize MATLAB for practical applications and complete projects efficiently.

gala
Télécharger la présentation

Probability Software Solution for Medical Segmentation

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. Applied Probability Lecture 4 Tina Kapur tkapur@ai.mit.edu

  2. Objective Use Probability to create a software solution to a real-world problem.

  3. Objective Use Probability to create a software solution to a real-world problem.

  4. Timeline/Administrivia • Friday: vocabulary, Matlab • Monday: start medical segmentation project • Tuesday: complete project • Wednesday: 10am exam • Lecture: 10am-11am, Lab: 11am-12:30pm • Homework (matlab programs): • PS 4: due 10am Monday • PS 5: due 12:30pm Tuesday

  5. Vocabulary • Random variable • Discrete vs. continuous random variable • PDF • Uniform PDF • Gaussian PDF • Bayes rule / Conditional probability • Marginal Probability

  6. Random Variable

  7. Random Variable • Function defined on the domain of an experiment

  8. Example r.v. • Experiment: 2 coin tosses • Sample space: • Random variable:

  9. Example r.v. • Experiment: 2 coin tosses • Sample space: HH, HT, TT, TH • Random variable: h number of heads in run

  10. Discrete vs. Continuous R. V.

  11. Discrete vs. Continuous R. V. • Domain

  12. PDF

  13. PDF • Function that associates probability values with events in sample space.

  14. PDF • Function that associates probability values with events in sample space. • Two characteristics of a PDF:

  15. PDF • Function that associates probability values with events in sample space. • Two characteristics of a PDF: • Mean or Expected value • Variance

  16. Uniform PDF

  17. Uniform PDF p(x) E(x) = s2(x) = ? 0 a x

  18. Gaussian PDF

  19. Gaussian PDF

  20. Bayes Rule Revisited

  21. Recitation/Lab • Install Matlab • Start Problem Set 1

More Related