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Article

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

1
IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
2
Faculty of Mathematics and Computer Science, Babeș–Bolyai University, 400084 Cluj-Napoca, Romania
3
Faculty of Physics, Babeș–Bolyai University, 400084 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Academic Editor: Leonardo Vanneschi
Entropy 2021, 23(4), 414; https://doi.org/10.3390/e23040414
Received: 6 March 2021 / Revised: 20 March 2021 / Accepted: 24 March 2021 / Published: 31 March 2021
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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. View Full-Text
Keywords: edge detection; evolutionary algorithms; cellular automata; particle swarm optimization; image processing; transfer learning; cardiac MRI edge detection; evolutionary algorithms; cellular automata; particle swarm optimization; image processing; transfer learning; cardiac MRI
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MDPI and ACS Style

Dumitru, D.; Dioșan, L.; Andreica, A.; Bálint, Z. A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization. Entropy 2021, 23, 414. https://doi.org/10.3390/e23040414

AMA Style

Dumitru D, Dioșan L, Andreica A, Bálint Z. A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization. Entropy. 2021; 23(4):414. https://doi.org/10.3390/e23040414

Chicago/Turabian Style

Dumitru, Delia, Laura Dioșan, Anca Andreica, and Zoltán Bálint. 2021. "A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization" Entropy 23, no. 4: 414. https://doi.org/10.3390/e23040414

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