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Knowledge and image processing algorithms for
real-life applications. Dr. Maria Athelogou
Principal Scientist & Scientific Liaison Manager
Email: [email protected]
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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
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Knowledge Based Image Analysis
A Knowledge-based system is a computer program that reasons and uses knowledge to solve complex problems.
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.
Knowledge Based Image Analysis: Experts create systems, which canuse 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
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Segmentation – Classificationfor 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).
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Segmentation
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Image
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Segmentation Algorithm
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Segmentation
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Image Features
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Classification (knowledge: Nuclei are blue and Cells are brown)
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Classification
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Automated Image Analysis and Quantification
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
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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
Cognition Network Language is a meta language for Image Analysis
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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.
Pixels are grouped into meaningful objects……which have defined
relationships to neighbours, across the same scale
relationships to bigger or smaller objects
Morphology/Geometry/ArchitectureIntensity
Creating a network of image objects2D/2D plus time/3D/3D plus time
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A lot of relational and often decisive context information is accessible: deeper insights, more data.
Semantic Network
Class Hierarchy
Process Network
Algorithms HierarchyImage Object Network
Definiens Cognition Network Technology
Cognition Network Language
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Semantic Network
Class HierarchyImage Object Network
Process Network
Algorithms Hierarchy
Definiens Cognition Network Technology
Cognition Network Language
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Context – Objects - Features
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Semantic Network-Class HierarchyClass definition using Fuzzy Logic
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Real Space / Size of Objects
1nm
1mm
1mm
10
100
10 100
0.1
10 1001m
10
100 1Km
10
Atom
transistor OrganPerson
Ship
CarHouse CityCell
Forest
m=meter
Traveling through the DimensionsReal World Applications
www.eCognition.com
Wide range of scientific areaswhich can make use of CNT
Slide - 21 Confidentialwww.definiens.com
Real Space / Size of Objects
1nm
1mm
1mm
10
100
10 100
0.1
10 1001m
10
100 1Km
10
Atom
transistor OrganPerson
Ship
CarHouse CityCell
Forest
m=meter
www.eCognition.com
One Technology – Many ApplicationsAutomatic Detection of Image Contents
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Electron Microscopy
TissueHigh Content Sreening
CellsCancer Biomarker
TissueProliferation index
Tissue
3D-Confocal Microscopy
Cell biology3D-Confocal Microscopy
Tissue Molecular Pathology
3D PET/CT
Small animal
One Technology – Many ApplicationsAutomatic Detection of Image Contents
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Slide - 24 Confidentialwww.definiens.comwww.definiens.com
Real Space / Size of Objects
1nm
1mm
1mm
10
100
10 100
0.1
10 1001m
10
100 1Km
10
Atom
transistor OrganPerson
Ship
CarHouse CityCell
Forest
m=meter
One Technology – Many ApplicationsAutomatic Detection of Image Contents
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BiopsyTissue
One Technology – Many ApplicationsAutomatic Detection of Image Contents
SerumCells
X-RayMammography
CTLiver tumor
CTBlood vessels
CTHead/Neck
MRIVentricles
CTLymph Nodes
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Slide - 27 Confidentialwww.definiens.comwww.definiens.com
Real Space / Size of Objects
1nm
1mm
1mm
10
100
10 100
0.1
10 1001m
10
100 1Km
10
Atom
transistor OrganPerson
Ship
CarHouse CityCell
Forest
m=meter
One Technology – Many ApplicationsAutomatic Detection of Image Contents
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Geo: Impervious Surface Maps Generation www.eCognition.com
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Thank You for Your Attention
