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GP-based Image Segmentation (GPIS) with Applications to Biomedical Image Segmentation

GP-based Image Segmentation (GPIS) with Applications to Biomedical Image Segmentation. Problem statement. How do we select an optimal sequence of low-level image operators (& parameters) to get the segmented image?. Segmentation example: cell nuclei. …. Model description.

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GP-based Image Segmentation (GPIS) with Applications to Biomedical Image Segmentation

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  1. GP-based Image Segmentation (GPIS) withApplications to Biomedical Image Segmentation (c) Louis Charbonneau and Nawwaf Kharma, 2009

  2. Problem statement • How do we select an optimal sequence of low-level image operators (& parameters) to get the segmented image? (c) Louis Charbonneau and Nawwaf Kharma, 2009

  3. Segmentation example: cell nuclei (c) Louis Charbonneau and Nawwaf Kharma, 2009

  4. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  5. … (c) Louis Charbonneau and Nawwaf Kharma, 2009

  6. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  7. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  8. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  9. Model description • We use Cartesian GP: • Primitive operators are clearly defined, their right combination is the problem • CGP allows for an easy interpretation of the resulting sequence • Segmentation is a class of problems without one perfect solution; CGP can handle this (c) Louis Charbonneau and Nawwaf Kharma, 2009

  10. System objectives • Effectiveness: segmentation should be correct • Efficiency: The smallest number of operations • Transparency: operation sequences should be easy to understand (c) Louis Charbonneau and Nawwaf Kharma, 2009

  11. System objectives (cont.) • Segmentation should be doable without a priori information (except for training ground truths) • Generality: effective on wide classes of images • Ease of Use: Minimal human intervention (c) Louis Charbonneau and Nawwaf Kharma, 2009

  12. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  13. Fitness criterion (c) Louis Charbonneau and Nawwaf Kharma, 2009

  14. Fitness criterion (c) Louis Charbonneau and Nawwaf Kharma, 2009

  15. Fitness criterion (c) Louis Charbonneau and Nawwaf Kharma, 2009

  16. Crossover (c) Louis Charbonneau and Nawwaf Kharma, 2009

  17. Mutations (I) (c) Louis Charbonneau and Nawwaf Kharma, 2009

  18. Mutations (II) (c) Louis Charbonneau and Nawwaf Kharma, 2009

  19. (c) Louis Charbonneau and Nawwaf Kharma, 2009

  20. Data 1026 images, 512 x 384 pixels 120 images, 340 x 780 pixels (c) Louis Charbonneau and Nawwaf Kharma, 2009

  21. System settings, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  22. Pixel segmentation accuracy, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  23. Cell segmentation accuracy, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  24. Statistical results, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  25. Example of evolved program, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  26. Example of evolved program, database 1 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  27. System settings, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  28. Pixel segmentation accuracy, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  29. Cell segmentation accuracy, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  30. Statistical results, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  31. Example of evolved program, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  32. Intermediate steps of evolved program, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  33. Intermediate steps of evolved program, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  34. Intermediate steps of evolved program, database 2 (c) Louis Charbonneau and Nawwaf Kharma, 2009

  35. Superimposed input + evolved program (c) Louis Charbonneau and Nawwaf Kharma, 2009

  36. GPIS on other types of images tree detection Lane detection (c) Louis Charbonneau and Nawwaf Kharma, 2009

  37. GPIS on other types of images Intra-cellular content of Wright-stained white blood cell images (c) Louis Charbonneau and Nawwaf Kharma, 2009

  38. Conclusion • CGP was able to adapt to the complexity of input images: • A short program was evolved to solve the easy problem • a longer program was evolved to solve the harder problem • Operator pool can be extended with specialized operators • Injection was a reliable means of maintaining population diversity (c) Louis Charbonneau and Nawwaf Kharma, 2009

  39. Conclusion • A training window approach is very effective for operator refinement • A small but accurate set of ground truths is enough to evolve segmentation algorithms without a priori information on the images (c) Louis Charbonneau and Nawwaf Kharma, 2009

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