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

Delia Dumitru¹, Laura Dioșan^1,2, Anca Andreica^1,2

¹IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania.

Entropy (Basel, Switzerland)

|April 3, 2021

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This summary is machine-generated.

This study optimizes edge detection for medical images using transfer learning with cellular automata (CA) and particle swarm optimization (PSO). The method improves accuracy for structures like cardiac cavities, aiding diagnostic tools.

Area of Science:

Computer Vision
Medical Image Analysis
Artificial Intelligence

Background:

Edge detection is crucial for image analysis but current methods have limitations like disconnected or branching edge detection.
The need for expert-annotated ground truth data is a significant challenge, especially in medical imaging.

Purpose of the Study:

To develop an adaptive edge detection method optimized for specific image properties.
To overcome limitations of existing edge detectors by improving accuracy and reducing reliance on ground truth data.
To adapt edge detection techniques for medical imaging applications where data is scarce.

Main Methods:

Utilizing transfer learning to optimize cellular automata (CA) rules for edge detection.
Employing particle swarm optimization (PSO) to adapt CA rules to target image characteristics.

Keywords:

cardiac MRI cellular automata edge detection evolutionary algorithms image processing particle swarm optimization transfer learning

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Applying the method to synthetic datasets and then transferring the learned rules to medical images.

Main Results:

Demonstrated the tunability of the optimized CA rules for diverse medical image properties.
Showcased that batch optimization can enhance edge quality for complex detection tasks.
Successfully identified structures like cardiac cavities in medical images.

Conclusions:

The proposed transfer learning approach effectively optimizes cellular automata for improved edge detection in medical images.
The method offers adaptability and enhanced performance, particularly for challenging medical imaging scenarios.
This technique holds potential for integration into automated radiology decision support systems.