Detector of interest point from region of interest on NBI endoscopy images

1. Detector of Interest Point within
Region of Interest on NBI Endoscopy
Images
Speaker: Stepanov Dmitry
Mizgulin V.V., Kosulnikov V.V., Kadushnikov R.M., Fedorov E.D., Buntseva О.А.
The work was done within the framework of the project performed by SIAMS company, and supported by the Ministry of Education
and Science of the Russian Federation (Grant agreement 14.576.21.0018 dated June 27, 2014). Project (applied research) unique ID
RFMEFI57614X0018.

2. Introduction
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Gastric cancer is the second most lethal cancer in the world.
Cancer causes 20% of deaths in the European Region, being
the second most important cause of death and morbidity in
Europe after cardiovascular diseases with more than 3
million new cases and 1.7 million deaths each year. In many
cases cancer can be avoided, and early detection
substantially increases the chance of cure.

3. Sano Classification
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4. Kudo Classification
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5. Dinis-Ribeiro Classification
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6. Challenges
• The decision on the biopsy is made subjectively;
• high qualification of specialist is required;
• diagnostic is time-consuming;
• re-consultation is necessary;
• due to complexity of processed images existing solutions
require manual selection of interest area.
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7. Goal
We develop a decision support system for
gastrointestinal endoscopy with the following features:
• self-training;
• real time operation;
• full automation for the implemented algorithms.
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8. The required properties of the detector
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Such a detector should provide the greatest amount of
points captured within an interest area, and satisfy the
following criteria introduced in [1]:
• repeatability;
• distinctiveness / informativeness;
• locality;
• quantity;
• accuracy;
• efficiency.
1. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey.
Foundations and Trends R in Computer Graphics and Vision 3(3), 177–280 (2008)

9. Solution
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1. Building representation a scale-space;
2. At each level of the scale-space we select skeleton of
pit-pattern;
3. Nodes of skeletons were considered as interest points;
4. Selecting scale for each interest point using methods
proposed by Mikolajczyk and Schmid [1].
1. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In:
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International
Conference on. vol. 1, pp. 525–531. IEEE (2001)

10. Selection of skeleton of gastric mucosa
pit-patterns
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Source NBI image Skeleton of pit-pattern

11. Keypoints selection
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The expert-annotated image, and the selection of interest points

12. Results of comparing several popular
interest point detectors
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DoG LoG Harris -
Laplacian
Hessian of
Laplacian
Fast
Hessian
Skeleton
Nodes
detected 0,84% 0,10% 0,11% 0,10% 0,14% 0,15%
correct/
detected
35,64% 35,58% 39,65% 35,89% 40,6% 45,88%
Row 2: percentage of points for which a characteristic scale is detected.
Row 3: percentage of points for which a location within ROI with respect to detected
points.
Detectors were implemented using the lip-vireo library. The data set consisted of 200
images.

13. Benchmark of interest point detectors
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14. Example visualization Region of
Interest by interest points
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15. Summary
• We develop a detector of key points with the
greatest amount of allocated key points from
the region of interest;
• These points are invariant to uneven
brightness, geometric deformation, rotation
and scale.
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16. Perspectives
Further research goals include collection of
training samples for all diagnoses with
histological confirmation and application of
classification methods for endoscopic images.
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17. Thank you for attention!
Stepanov Dmitry
SIAMS Ltd, 2016