GPIS: Genetic Programming based Image Segmentation with Applications to

Biomedical Object Detection

Tarundeep Singh DhotDept of ECE, Concordia University, Montreal, Canada

t_dhot@encs.concordia.ca

Presentation Overview

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Image Segmentation Overview

The Proposed Algorithm - GPIS

Experiments

Results

Conclusions and Contributions

Future Work

Image Segmentation

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• 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

• 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

WHAT IS GPIS ?

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Representation

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• 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]

GPIS - Flowchart

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Initialization

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• Initial population of programs is randomly generated

• Maximum length of program = 15 operators

Fitness Function

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where:FPR – False Positive RateFNR – False Negative RateWp – Weight for False Positives, Wp ϵ [0, 0.5] Wn – Weight for False Negatives, Wn = 1 - Wp len = Length of the programβ – Scaling factor for the length of a program, β ϵ [0.004, 0.008]

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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Evolutionary Operators

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• Crossover: One-point

• Mutations:• Type A: Swap, Insert, Delete (Inter-genomic)• Type B: Alter (Intra-genomic)

[A1] [A2] [A3] [A4] [A5] [A6] [A7] [A1] [A2] [A3] [B6] [B7]

[DIL, d1, 0, 0, 2]

GENE

[A], [B] = IMAGE OPERATOR

[B1] [B2] [B3] [B4] [B5] [B6] [B1] [B2] [B3] [B4] [A4] [A5] [A6] [A7]

Crossover: 1-pointPARENT CHROMOSOMES OFFSPRING CHROMOSOMES

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Mutations: Swap, Insert, Delete, Alter

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Injection

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• 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

Termination

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• Termination is based on a fitness threshold (95% - Db1 and 90% - Db2)

• Termination criteria:|current fitness – mean fitness(10 gen)| < 0.05 * highest fitness

Experiments

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PARAMETER

DATABASE 1 (HeLa CELLS)

DATABASE 2 (LIVER TISSUE)

Total images

1026 120

Training set

30 25

Validation set

100 75

Runs 28 26

• 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

GENIE Pro

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• 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)

Results: Database 1

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Fitness = 97.38%

96.98%

97.12%

91.02%

86.44%

Superimposed input-evolved image

Results: Database 2

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Fitness = 92.12%Fitness = 93.29%

Superimposed input-evolved image

Results: Database 2

Fitness = 87.14%

Fitness = 89.44%

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Superimposed input-evolved image

Results:

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ALGORITHM

FITNESS (Accuracy)

Cell Detection

Rate

GPIS 96.11% 97.98%

GENIE Pro 95.50% 96.56%

(i) Effectiveness

# GENERATIO

NS

# FITNESS EVALUATIO

NSBEST

(HIGHEST FITNESS)

114 10,532

AVERAGE 122.07 11,257.67

(ii) Efficiency

ALGORITHM FITNESS (Accuracy)

Cell Detection

Rate

GPIS 88.98% 89.98%

GENIE Pro 85.80% 87.42%

# GENERATIO

NS

# FITNESS EVALUATION

SBEST

(HIGHEST FITNESS)

206 18,732

AVERAGE 214.15 19,563.87

Database 1: HeLa Cells

Database 2: Liver Tissue Specimen

Results: Evolved Programs

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Database 1: HeLa CellsA. [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

B. [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

A. [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

B. [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

Database 2: Liver Tissue Specimen

Results: Structure of Evolved Program

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[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]

GAUSS

AVER EROD AVER CLOP THRESH

Evolved chromosome

Gene structure of chromosome

Fitness of program on validation set = 97.32%# Generation = 114, # Fitness evaluations =

10,532

Input Image

Output Image

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);

MATLAB implementation

Conclusions

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• 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%)

Contributions

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• 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

Suggested Future Work

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• Inclusion of automatically defined functions

• Competitive co-evolution

• Addition of conditional jumps

Comments/Questions

Thank you!