This document summarizes a genetic programming based image segmentation tool called GPIS. GPIS uses genetic programming to evolve image segmentation algorithms from a pool of primitive image analysis operators. It has been tested on two medical image databases for cell and nuclei extraction. The results show that GPIS achieved high detection rates of over 97% for one database and around 90% for the other. The evolved programs consisted of combinations of image filtering, morphological, and thresholding operators. Future work could include adding automatically defined functions, competitive co-evolution, and conditional jumps to improve the tool.

1. GPIS: Genetic Programming based Image Segmentation with Applications to Biomedical Object DetectionTarundeep Singh DhotDept of ECE, Concordia University, Montreal, Canadat_dhot@encs.concordia.ca

2. Presentation OverviewSlide 2/26

3. Image Segmentation Is separation of objects/regions of interest from the background and each other

4. Foreground/background separation process

5. Vital first step of any image analysis process

6. Ill-defined problem – no general segmentation frameworkSlide 3/26Examples of image segmentation

7. WHAT IS GPIS ? A GP-based image segmentation tool

8. The GP evolves segmentation algorithms from a pool of primitive operators

9. Primitives: Low-level image analysis functions (arithmetic, spectral, morphological, etc) - 20 primitives used

10. GP searches for most effective combinations of primitives

11. Currently tested on two medical image databasesSlide 4/26

12. Linear chromosomal representation

13. Chromosomes - programs[HIST, d1, 0, 0, 0] [SUBP, io1, d1, .2, 0] [DIL, io2, 0, 0, 4] [LAPL, io3, 0, -4, 0] Genes – image operators[Operator, Input 1, Input 2, Weight, Structuring Element/Filter Parameter]RepresentationSlide 5/26

14. GPIS - FlowchartSlide 6/26

15. InitializationInitial population of programs is randomly generated

16. Maximum length of program = 15 operatorsSlide 7/26

17. Fitness Functionwhere:FPR – False Positive RateFNR – False Negative RateWp – Weight for False Positives, Wp ϵ [0, 0.5]Wn– Weight for False Negatives, Wn= 1 - Wplen = Length of the programβ – Scaling factor for the length of a program, β ϵ [0.004, 0.008]Slide 8/26

18. Selection and ElitismElitism: 1% of best individuals in population

19. Parent Selection: Tournament Selection

20. Tournament window size, λ= 10% of population size

21. Survivor Selection:

22. Steady State (no injection)

23. Fitness based (injection)Slide 9/26

24. Evolutionary Operators Crossover: One-point

25. Mutations:

26. Type A: Swap, Insert, Delete (Inter-genomic)