Authors:
Wen Shi
and
Shahram Payandeh
Affiliation:
Simon Fraser University, Canada
Keyword(s):
Image Segmentation, Shape recovery, SPH (Smoothed particle Hydrodynamics), ”Rain Fall” model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This paper studies the problem of shape recovery and image segmentation with examples related to medical imaging. Our purpose is to explore an alternative physics based image segmentation model in comparison with parametric intensive methods such as active contour or level set approaches. The proposed model can offer a more computational efficient approach. As an early attempt, a novel segmentation method based on physically motivated particle system is presented, analyzed and integrated for 2D and 3D applications. Different from previous particle based segmentation method, our proposed approach is governed physically by fluid dynamic model. Additionally a novel "rain fall" model is presented as an alternative paradigm for shape reconstruction and image segmentation when working with complex 2D and 3D medical images. In this paper, an overview of fluid mechanical model and fluid particle simulation process is presented as well. Segmentation results on 2D images and shape recovery of
3D images are presented followed by discussions and conclusions.
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