Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Authors = Laura Dioșan

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 1148 KiB  
Article
A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization
by Delia Dumitru, Laura Dioșan, Anca Andreica and Zoltán Bálint
Entropy 2021, 23(4), 414; https://doi.org/10.3390/e23040414 - 31 Mar 2021
Cited by 9 | Viewed by 2916
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
Show Figures

Figure 1

Back to TopTop