dmitry stepanov - detector of interest point from region of interest on nbi endoscopy images

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.

Introduction

2

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.

Sano Classification

3

Kudo Classification

4

Dinis-Ribeiro Classification

5

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.

6

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.

7

The required properties of the detector

8

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)

Solution

9

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)

Selection of skeleton of gastric mucosa pit-patterns

10

Source NBI image Skeleton of pit-pattern

Keypoints selection

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

Results of comparing several popular interest point detectors

12

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.

Benchmark of interest point detectors

13

Example visualization Region of Interest by interest points

14

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.

15

Perspectives

Further research goals include collection of training samples for all diagnoses with histological confirmation and application of classification methods for endoscopic images.

16

Thank you for attention!

Stepanov DmitrySIAMS Ltd, 2016