11am - 12 noon

Friday 17 May 2024

Computational Techniques for Digital Mapping and Correction of Metamorphopsia

PhD Viva Open Presentation for Ye Ling

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University of Surrey
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Computational Techniques for Digital Mapping and Correction of Metamorphopsia

Ye Ling



This doctoral thesis presents a comprehensive study in the field of visual impairment, focusing on the development and validation of innovative tests for visual distortion led by metamorphopsia. The core of this research involves the creation and evaluation of four novel tests: the Image Warping Test, the Feature Alignment Test, the Symmetry Completion Test, and the Image Comparison Test. These tests were designed to enhance the accuracy and sensitivity of visual distortion assessments.

The Image Warping Test stands out for its interactive methodologies, allowing patients to actively participate in the assessment of their visual distortions. The Feature Alignment Test is a new attempt in the field of binocular testing. The Symmetry Completion Test introduces a novel approach using low-level geometric constraints for distortion mapping, while the Image Comparison Test further advances this method by integrating Interactive Genetic Algorithms, improving the stability by removing human input errors.


Clinical trials were conducted at St Thomas’ Hospital, London, involving participants with diagnosed metamorphopsia. These trials were crucial in validating the effectiveness of the tests in a real-world clinical setting and gathering essential patient feedback to refine the test designs. The research adhered to the highest ethical standards, aligning with the World Medical Association Declaration of Helsinki.


Despite the success of these tests in accurately assessing visual distortions, the research identified certain limitations, particularly in handling complex distortions and addressing central point distortions. Future directions for this work include developing more advanced algorithms, focusing on central vision distortions, expanding the diversity of clinical trial participants, introducing dynamic correction, and integrating emerging technologies like AI for automated diagnostics.

In summary, this thesis makes significant contributions to the field of visual distortion, offering new, more effective tools for clinicians and enhancing our understanding of visual distortions. Its findings lay a foundation for future research, with the potential to greatly improve the quality of life for individuals with visual impairments.