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Elastic models: a comparative study applied to retinal images.

E Karali¹, S Lambropoulou, D Koutsouris

¹National Technical University of Athens, Department of Electrical and Computer Engineering, Biomedical Engineering Laboratory, Zografou Campus, Athens, Greece. ekarali@biosim.ntua.gr

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|December 2, 2011

Summary

This summary is machine-generated.

The self-affine mapping system effectively segments optic disks in glaucomatous retinal images, offering superior accuracy and speed compared to snake models. This method shows independence from initialization for better results.

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Area of Science:

Medical Imaging
Computer Vision
Biomedical Engineering

Background:

Accurate segmentation of the optic disk in retinal images is crucial for diagnosing glaucoma.
Parametric elastic models like snakes are commonly used but have limitations.

Purpose of the Study:

To compare the performance of various elastic models (snake, GVF snake, t-snake) against a self-affine mapping system for optic disk segmentation.
To evaluate segmentation accuracy, speed, and initialization independence.

Main Methods:

Comparison of classical snake, gradient vector field (GVF) snake, and topology-adaptive (t-snake) models.
Implementation of a self-affine mapping system using an adapting scheme and minimum distance optimization.
Application of all methods to glaucomatous retinal images for optic disk segmentation.

Main Results:

The self-affine mapping system demonstrated adequate segmentation time and accuracy.
This method showed significant independence from initialization, a limitation for other models.
Performance was evaluated using cross-correlation coefficients for accuracy and segmentation time.

Conclusions:

The self-affine mapping system is a viable and effective alternative to elastic models for optic disk segmentation.
Its robustness to initialization and balanced performance make it suitable for clinical applications.
Further research can explore its application in other medical image segmentation tasks.