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Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images.

Xiaozhong Xue¹, Linni Wang², Weiwei Du¹

¹Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

Accurate optic disc segmentation in fundus images is vital for diagnosing retinal diseases. A novel hybrid level set model with multiple preprocessing steps effectively addresses challenges like low contrast and anatomical variations, improving segmentation accuracy.

Keywords:

four-side evaluation hybrid level set multiple preprocessing optic disc segmentation wide-angle fundus images

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate optic disc (OD) segmentation is essential for diagnosing retinal diseases.
Challenges include vascular occlusion, parapapillary atrophy (PPA), and low contrast in fundus images.

Purpose of the Study:

To propose a novel multiple preprocessing hybrid level set model (HLSM) for robust OD segmentation.
To enhance segmentation accuracy in the presence of image artifacts and low-quality fundus images.

Main Methods:

A hybrid level set model (HLSM) incorporating area-based and shape-based terms was developed.
Multiple preprocessing techniques were integrated to handle image variations.
Area-based term: difference of average pixel values inside/outside contour.
Shape-based term: distance to a prior shape model.

Main Results:

Achieved an average Intersection over Union (IoU) of 0.9275 and average Four-Side Evaluation (FSE) of 4.6426 on narrow-angle fundus images.
Obtained an average IoU of 0.8179 and average FSE of 3.5946 on wide-angle fundus images.
Demonstrated effectiveness in segmenting the optic disc despite image challenges.

Conclusions:

The proposed multiple preprocessing HLSM is an effective method for optic disc segmentation.
The model shows promise for clinical applications in retinal disease analysis.
Addresses key limitations of existing OD segmentation techniques.