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

2. Presentation Overview
Image Segmentation Overview
The Proposed Algorithm - GPIS
Experiments
Results
Conclusions and Contributions
Future Work

3. Examples of 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
Background info of IS
Images – easy to relate the concept
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
So in this context, what is GPIS. Based on given knowledge of IS, now its easy to provide an idea of GPIS
Also explain GP

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)
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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
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12. Mutations: Swap, Insert, Delete, Alter
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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. DATABASE 2 (LIVER TISSUE)
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
PARAMETER
DATABASE 1 (HeLa CELLS)
DATABASE 2 (LIVER TISSUE)
Total images
1026
120
Training set 30 25
Validation set
100 75
Runs 28 26
Explain 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%
Lower accuracy due to partial detection
91.02%
86.44%

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

19. Results: Database 2 Superimposed input-evolved image Fitness = 89.44%
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20. BEST (HIGHEST FITNESS) BEST (HIGHEST FITNESS)
Results:
Database 1: HeLa Cells
Database 2: Liver Tissue Specimen
ALGORITHM
FITNESS (Accuracy)
Cell Detection Rate
GPIS
96.11%
97.98%
GENIE Pro
95.50%
96.56%
ALGORITHM
FITNESS (Accuracy)
Cell Detection Rate
GPIS
88.98%
89.98%
GENIE Pro
85.80%
87.42%
(i) Effectiveness
# GENERATIONS
# FITNESS EVALUATIONS
BEST (HIGHEST FITNESS)
114
10,532
AVERAGE
122.07
11,257.67
# GENERATIONS
# FITNESS EVALUATIONS
BEST (HIGHEST FITNESS)
206
18,732
AVERAGE
214.15
19,563.87
(ii) Efficiency

21. Results: Evolved Programs
Database 1: HeLa Cells
[GAUSS, d1, 0, 6, ] [AVER, io1, 0, 4, 0] [EROD, io2, 0, 0, 1] [AVER, io3, 0, 6, 0] [CLOP, io4, 0, 0, 1]
[THRESH, io5, 0, , 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]
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]
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
GAUSS
AVER
EROD
CLOP
THRESH
Gene structure of chromosome
[GAUSS, d1, 0, 6, ]
[AVER, io1, 0, 4, 0]
[EROD, io2, 0, 0, 1]
[AVER, io3, 0, 6, 0]
[CLOP, io4, 0, 0, 1]
[THRESH, io5, 0, , 0]
MATLAB implementation
d1 = input;
h1 = fspecial(‘gaussian’, [6 6], ) ;
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, );
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!