Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

2010 ◽  
Author(s):  
Ahmed Afifi ◽  
Toshiya Nakaguchi ◽  
Norimichi Tsumura
Keyword(s):  
Particle Swarm Optimization ◽  
Medical Images ◽  
Particle Swarm ◽  
Shape Priors ◽  
Swarm Optimization

scholarly journals A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 414
Author(s):  
Delia Dumitru ◽  
Laura Dioșan ◽  
Anca Andreica ◽  
Zoltán Bálint
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Automata ◽  
Edge Detection ◽  
Transfer Learning ◽  
Medical Images ◽  
Particle Swarm ◽  
Ground Truth ◽  
Swarm Optimization ◽  
Support Tool ◽  
Edge Detectors

Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool.

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