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

GP-based Image Segmentation (GPIS)

withApplications to Biomedical

Image Segmentation

(c) Louis Charbonneau and Nawwaf Kharma, 2009

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• 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

System objectives • Effectiveness: segmentation should be correct• Efficiency: The smallest number of operations• Transparency: operation sequences should be easy to understand

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

Fitness criterion

Crossover

Mutations (I)

Mutations (II)

1026 images, 512 x 384 pixels

120 images, 340 x 780 pixels

System settings, database 1

Pixel segmentation accuracy, database 1

Cell segmentation accuracy, database 1

Statistical results, database 1

Example of evolved program, database 1

System settings, database 2

Pixel segmentation accuracy, database 2

Cell segmentation accuracy, database 2

Statistical results, database 2

Intermediate steps of evolved program, database 2

Superimposed input + evolved program

GPIS on other types of images

Lane detection tree detection

GPIS on other types of images

Intra-cellular content of Wright-stained white blood cell images

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

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

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

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

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

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

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