Authors:
H. Djaghloul
1
;
M. Batouche
2
and
J. P. Jessel
3
Affiliations:
1
Ferhat Abbes University, Algeria
;
2
King Saoud University, Saudi Arabia
;
3
IRIT, France
Keyword(s):
Markerless augmented reality, Wavelets, Multi-resolution analysis, Evolutionary algorithms, Swarm intelligence.
Related
Ontology
Subjects/Areas/Topics:
Augmented, Mixed and Virtual Environments
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Interactive Environments
;
Modeling and Algorithms
;
Modeling of Natural Scenes and Phenomena
;
Multi-Resolution Modeling
;
Physics-Based Modeling
;
Scene and Object Modeling
;
Solid and Heterogeneous Modeling
Abstract:
In this paper we present an augmented reality system for laparoscopic cholecystectomy video sequences enhancing. Augmented reality allows surgeons to view, in transparency, occluded anatomical and pathological structures constructed preoperatively using medical images such as MRI or CT-Scan. The deformable nature of digestive organs leads to a high dimensionality N-degrees of freedom detection and tracking problem. We describe a knowledge-based construction method of powerful statistical color models for anatomical structures and surgical instruments classification. Thanks to a new wavelet based multi-resolution analysis of the virtual reality models and the anatomical color space; we can detect and track digestive organs to ensure marker-less laparoscopic monocular camera pose and preoperative 3D model registration. Results are shown on both synthetic and real data.