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Tarundeep Singh Dhot Dept of ECE, Concordia University, Montreal, Canada t_dhot@encs.concordia.ca

GPIS: Genetic Programming based Image Segmentation with Applications to Biomedical Object Detection. Tarundeep Singh Dhot Dept of ECE, Concordia University, Montreal, Canada t_dhot@encs.concordia.ca. Presentation Overview. Image Segmentation.

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Tarundeep Singh Dhot Dept of ECE, Concordia University, Montreal, Canada t_dhot@encs.concordia.ca

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  1. GPIS: Genetic Programming based Image Segmentation with Applications to Biomedical Object Detection Tarundeep Singh Dhot Dept of ECE, Concordia University, Montreal, Canada t_dhot@encs.concordia.ca

  2. Presentation Overview

  3. Image Segmentation • Is separation of objects/regions of interest from the background and each other • Foreground/background separation process • Vital first step of any image analysis process • Ill-defined problem – no general segmentation framework Examples of image segmentation

  4. WHAT IS GPIS ? • A GP-based image segmentation tool • The GP evolves segmentation algorithms from a pool of primitive operators • Primitives: Low-level image analysis functions (arithmetic, spectral, morphological, etc) - 20 primitives used • GP searches for most effective combinations of primitives • Currently tested on two medical image databases

  5. Representation • Linear chromosomal representation • 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]

  6. GPIS - Flowchart

  7. Initialization • Initial population of programs is randomly generated • Maximum length of program = 15 operators

  8. Fitness Function where: FPR – False Positive Rate FNR – False Negative Rate Wp – Weight for False Positives, Wp ϵ [0, 0.5] Wn– Weight for False Negatives, Wn= 1 - Wp len = Length of the program β – Scaling factor for the length of a program, β ϵ [0.004, 0.008]

  9. Selection and Elitism • Elitism: 1% of best individuals in population • Parent Selection: Tournament Selection • Tournament window size, λ= 10% of population size • Survivor Selection: • Steady State (no injection) • Fitness based (injection) Slide 9/26

  10. Evolutionary Operators • Crossover: One-point • Mutations: • Type A: Swap, Insert, Delete (Inter-genomic) • Type B: Alter (Intra-genomic)

  11. Crossover: 1-point PARENT CHROMOSOMES OFFSPRING CHROMOSOMES [A1] [A2] [A3] [A4] [A5] [A6] [A7] [A1] [A2] [A3] [B6] [B7] [B1] [B2] [B3] [B4] [B5] [B6] [B1] [B2] [B3] [B4] [A4] [A5] [A6] [A7] GENE [DIL, d1, 0, 0, 2] [A], [B] = IMAGE OPERATOR Slide 11/26

  12. Mutations: Swap, Insert, Delete, Alter Slide 12/26

  13. Injection • Every 5 generations, randomly initialized programs injected into population • Number of injected program = 20% of population size • Injection used in order to maintain population diversity

  14. Termination • Termination is based on a fitness threshold (95% - Db1 and 90% - Db2) • Termination criteria: • |current fitness – mean fitness(10 gen)| < 0.05 * highest fitness

  15. Experiments • Tested on 2 medical image databases (HeLa Cells, Liver Tissue Specimen) • Database 1: Cell extraction • Database 2: Nuclei extraction • Tested for effectiveness and efficiency • Results compared to a GA-based image segmentation tool GENIE Pro

  16. GENIE Pro • GA based general purpose image segmentation/feature extraction software • Manual highlighting to prepare ground truth (true, false and unknown pixels) • GA evolves IP “pipelines” – sequence of IP functions for segmentation from a set of IP functions based on prepared ground truth • Evolved programs are constructed by combining the fittest pipelines using a linear classifier (Fisher Discriminant)

  17. Results: Database 1 Superimposed input-evolved image Fitness = 97.38% 97.12% 96.98% 86.44% 91.02%

  18. Results: Database 2 Superimposed input-evolved image Fitness = 93.29% Fitness = 92.12%

  19. Results: Database 2 Superimposed input-evolved image Fitness = 89.44% Fitness = 87.14% Slide 19/26

  20. Results: Database 1: HeLa Cells Database 2: Liver Tissue Specimen (i) Effectiveness (ii) Efficiency

  21. Results: Evolved Programs Database 1: HeLa Cells • [GAUSS, d1, 0, 6, 0.8435] [AVER, io1, 0, 4, 0] [EROD, io2, 0, 0, 1] [AVER, io3, 0, 6, 0] [CLOP, io4, 0, 0, 1] • [THRESH, io5, 0, 0.09022, 0] • Fitness on validation set = 97.32%, Number of operators = 6 • [DISK, d1, 0, 3, 0] [AVER, io1, 0, 6, 0] [CLOSE, io2, 0, 0, 2] [ADDP, io1, io2, 0, 0] [EROD, io3, 0, 1] [EROD, io4, 0, 1] [THRESH, io5, 0, 0.1264, 0] • Fitness on validation set = 97.61%, Number of operators = 7 Database 2: Liver Tissue Specimen • [LOWPASS, d1, 0, 32, 0.793] [AVER, io1, 0, 4, 0] [AVER, io2, 0, 3, 0] [ADJUST, io3, 0, .205, 0.517] • [CLOSE, io4, 0, 0, 1] [THRESH, io5, 0, 0.9852, 0] • Fitness on validation set = 91.89%, Number of operators = 6 • [UNSHARP, d1, 0, 0.82, 0] [HIST, io4, 0, 0, 0] [LAPL, io1, 0, -8, 0] [DISK, io2, 0, 6, 0] [AVER, io3, 0, 6, 0] • [HIST, io4, 0, 0, 0] [ADJUST, io5, 0, 0, 0.202] [OPEN, io6, 0, 0, 1] [EROD, io7, 0, 0, 1] [THRESH, io8, 0, 0.752, 0] • Fitness on validation set = 92.17%, Number of operators = 10

  22. Results: Structure of Evolved Program Evolved chromosome Gene structure of chromosome [GAUSS, d1, 0, 6, 0.8435] [AVER, io1, 0, 4, 0] [EROD, io2, 0, 0, 1] [AVER, io3, 0, 6, 0] [CLOP, io4, 0, 0, 1] [THRESH, io5, 0, 0.09022, 0] MATLAB implementation d1 = input; h1 = fspecial(‘gaussian’, [6 6], 0.8435) ; io1 = imfilter(d1, h1); h2 = fspecial(‘average’, [4 4]); io2 = imfilter(io1,h2); SE1 = strel(‘disk’, 2); io3 = imerode(io2, SE1); h3 = fspecial(‘average’, [6 6]); io4 = imfilter(io3,h3); io5 = imclose(io4, SE1); output = im2bw(io5, 0.09022); Input Image Fitness of program on validation set = 97.32% # Generation = 114, # Fitness evaluations = 10,532 Output Image

  23. Conclusions • Experimental results show that the operator pool is sufficient for our databases • Injection is a viable option to maintain diversity • Mutation is desirable as it allows parameter tuning • GP was able to learn complexity of the databases in use (less generations for convergence for Db1 as compared to Db2) • GP showed high detection rates on both databases (Db1 = 97.98%, Db2 = 89.98%)

  24. Contributions • Simple approach and anyone with MATLAB can use it • Open sourced code • Requires no a priori information other than training images • Relatively general approach based on results on the two databases • Produces better results as compared to GENIE Pro

  25. Suggested Future Work • Inclusion of automatically defined functions • Competitive co-evolution • Addition of conditional jumps

  26. Comments/Questions Thank you!

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