1. 19 April, 2017
Knowledge and image processing algorithms for real-life applications. Dr. Maria Athelogou Principal Scientist & Scientific Liaison Manager

2. Artificial intelligence (AI) is the technology and a branch of computer science that studies and develops intelligent machines and software. 
Goals
Knowledge Based Systems
Speech Recognition
Pattern Recognition and Pattern Analysis / Image Analysis
Robotics
Tools
Search and Optimization
Planning
Logic
Probabilistic Methods
Classifiers and statistical Learning
Neural Networks
Control theory
Languages

3. Knowledge Based Image Analysis
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques.
Problem:
Many powerful programs and many different program libraries have been developed. In such libraries the individual programs are integrated from a low level point of view. But not support is provided to users who need to solve practical image processing problems. Every end – user cannot have a deep understanding of program semantic and syntax.
A Knowledge-based system is a computer program  that reasons and uses knowledge to solve complex problems.
Knowledge Based Image Analysis: Experts create systems, which can
use knowledge to compose complex image analysis processes from primitive
image processing operators.
These systems have a Graphical User Interface, which enables the users to use
the systems in order to solve image analysis tasks.
Example: GNORASI

4. Segmentation – Classification for Image Analysis
Segmentation:  the partitioning of a digital image into two or more regions
Classification:  analyzes the numerical properties of various image features and organizes data into categories (Classes).

5. Segmentation

6. Image
19 April, 2017

7. Segmentation Algorithm

8. Segmentation

9. Image Features

10. Classification (knowledge: Nuclei are blue and Cells are brown)

11. Classification

12. 19 April, 2017
Using Knowledge for Classification (Experts: different kinds of Nuclei according to their Context)

13. Automated Image Analysis and Quantification
19 April, 2017
The Cognition Network Technology emulates the way the human mind understands images
Definiens Cognition Network Technology® examines pixels in context to extract intelligence from images.
Multiscale
Object based
Context based
Knowledge driven
Find all relevant objects and their mutual relations
create a hierarchical linked object structure referred to as „segmentation“
Link these objects with the knowledge about them and their relations
knowledge representation
hierarchical classification
In the context of this technology (CNT) Image Analysis is an Iterative process:
Alternation between Segmentation and Classification

14. Cognition Network Language is a meta language for Image Analysis
19 April, 2017
Cognition Network Language is a meta language for Image Analysis
CNT - Image analysis system has a GUI
This GUI contains this CNT - meta language that allows for fast and efficient development of rule bases
A rule base addresses the solution of a specific image analysis task
A rule base is reusable

15. Creating a network of image objects 2D/2D plus time/3D/3D plus time
19 April 2017
19 April, 2017
Pixels are grouped into meaningful objects…
…which have defined
relationships to neighbours, across the same scale
relationships to bigger or smaller objects
Morphology/Geometry/Architecture
Intensity
Operating on this network: “localized” decision making can account for biological heterogeneity
A lot of relational and often decisive context information is accessible: deeper insights, more data. 15

16. Definiens Cognition Network Technology Cognition Network Language
19 April, 2017
19 April 2017
Definiens Cognition Network Technology
Cognition Network Language
Process Network
Algorithms Hierarchy
Image Object Network
Semantic Network
Class Hierarchy
A lot of relational and often decisive context information is accessible: deeper insights, more data. 16

17. Definiens Cognition Network Technology Cognition Network Language
Process Network
Algorithms Hierarchy
Semantic Network
Class Hierarchy
Image Object Network

18. Context – Objects - Features

19. Semantic Network-Class Hierarchy
Class definition using Fuzzy Logic

20. Real Space / Size of Objects
19 April, 2017
Traveling through the Dimensions
Real World Applications
Real Space / Size of Objects
Wide range of scientific areas
which can make use of CNT
m=meter
0.1
1nm 10
Atom
100
100 10 1m
transistor
1mm
Organ
1mm 10
Person 10 10
Cell
100
Car
City
House
1Km
100
Forest
Ship 20 20 20

21. Real Space / Size of Objects
19 April, 2017
One Technology – Many Applications
Automatic Detection of Image Contents
Real Space / Size of Objects
m=meter
0.1
1nm 10
Atom
100
100 10 1m
transistor
1mm
Organ
1mm 10
Person 10 10
Cell
100
Car
City
House
1Km
100
Forest
Ship 21 21 21

22. Electron Microscopy Tissue
19 April, 2017
One Technology – Many Applications
Automatic Detection of Image Contents
Electron Microscopy Tissue
High Content Sreening Cells
Proliferation index Tissue
Cancer Biomarker Tissue
3D-Confocal Microscopy Tissue Molecular Pathology
3D-Confocal Microscopy Cell biology
3D PET/CT Small animal 22 22 22

23. 19 April, 2017

24. Real Space / Size of Objects
19 April, 2017
One Technology – Many Applications
Automatic Detection of Image Contents
Real Space / Size of Objects
m=meter
0.1
1nm 10
Atom
100
100 10 1m
transistor
1mm
Organ
1mm 10
Person 10 10
Cell
100
Car
City
House
1Km
100
Forest
Ship 24 24 24

25. One Technology – Many Applications
19 April, 2017
One Technology – Many Applications
Automatic Detection of Image Contents
CT Head/Neck
Biopsy Tissue
Serum Cells
X-Ray Mammography
CT Liver tumor
CT Blood vessels
MRI Ventricles
CT Lymph Nodes 25 25 25

26. 19 April, 2017

27. Real Space / Size of Objects
19 April, 2017
One Technology – Many Applications
Automatic Detection of Image Contents
Real Space / Size of Objects
m=meter
0.1
1nm 10
Atom
100
100 10 1m
transistor
1mm
Organ
1mm 10
Person 10 10
Cell
100
Car
City
House
1Km
100
Forest
Ship 27 27 27

28. Geo: Impervious Surface Maps Generation

29. Thank You for Your Attention