Authors:
Yann Gavet
1
;
Jean-Charles Pinoli
1
;
Gilles Thuret
2
and
Philippe Gain
2
Affiliations:
1
Centre Ingénierie et Santé & Laboratoire LPMG, École Nationale Supérieure des Mines, France
;
2
Faculté de Médecine, France
Keyword(s):
Pattern recognition, segmentation, grouping, cognitive and biologically inspired computer vision, mathematical morphology, medical image analysis, computational geometry, Gestalt Theory, Contour closure, Mosaic reconstruction, image analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Medical Image Analysis
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Surface Geometry and Shape
Abstract:
The human visual system is far more efficient than a computer to analyze images, especially when noise or poor acquisition process make the analysis impossible by lack of information. To mimic the human visual system, we develop algorithms based on the gestalt theory principles: proximity and good continuation. We also introduce the notion of mosaic that we reconstruct with those principles. Mosaics can be defined as geometry figures (squares, triangles), or issued from a contour detection system or a skeletonization process. The application presented here is the detection of cornea endothelial cells. They present a very geometric structure that give enough information for a non expert to be able to perform the same analysis as the ophthalmologist, that mainly consists on counting the cells and evaluating the cell density.