A Novel Image Registration Framework for Monitoring Diabetic Retinopathy Progression with Genetic Algorithm-Based Image Alignment

Authors

  • Prajakta Patil Assistant Professor Dept. of Ophthalmology Krishna Vishwa Vidyapeeth, Karad, Maharashtra, India
  • Renuka Sarwate Dept. of Ophthalmology Krishna Vishwa Vidyapeeth, Karad, Maharashtra, India
  • Amit Kumar Mishra Department of Computer Science and Engineering, Graphic Era Hill University Dehradun, Uttarakhand, India,
  • Himanshu Rai Goyal Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002

Keywords:

Genetic Algorithm, Diabetic Retinopathy Progression, Disease Progression, Retinopathy

Abstract

Millions of people worldwide suffer from diabetic retinopathy (DR), a crippling eye condition that can damage vision and even blindness if left untreated. For prompt intervention and efficient care, early recognition and monitoring of DR development are essential. In this paper, we introduce a novel image registration framework that makes use of genetic algorithms (GAs) to precisely and automatically align retinal images, enabling the tracking of DR progression. Our method tackles the difficulties in longitudinally monitoring minute retinal changes, which are crucial for comprehending disease progression and informing therapeutic approaches.Due to differences in picture quality, lighting, and the presence of diseases such microaneurysms and exudates, traditional image registration algorithms frequently struggle to handle the complexity of retinal image alignment. We use GAs, which are excellent at optimising non-linear and multi-modal objective functions, to get around these difficulties. Our framework looks for the ideal transformation parameters that precisely align the baseline and follow-up retinal images by treating image registration as an optimisation problem.In order to improve registration accuracy and sensitivity to pathological alterations, the proposed system proposes a novel objective function that blends pixel intensity-based similarity metrics with structural elements unique to DR disease. Additionally, a genetic algorithm-driven optimisation procedure enables the simultaneous adjustment of several transformation parameters, offering robustness in the face of intricate retinal deformations.Our research on a large dataset of retinal scans shows that the GA-based method aligns images more precisely than conventional registration techniques. Through quantitative evaluations and visual inspections, the framework's efficiency in tracking DR progression is confirmed, demonstrating its potential for early disease diagnosis and monitoring minute pathological changes over time.

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Published

04.11.2023

How to Cite

Patil, P. ., Sarwate, R. ., Mishra, A. K. ., & Goyal, H. R. . (2023). A Novel Image Registration Framework for Monitoring Diabetic Retinopathy Progression with Genetic Algorithm-Based Image Alignment. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 500–509. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3730

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

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