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Anatomy of the Eyeball

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Updated: May 14, 2026

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Published on: August 4, 2018

Automatic localization of retinal landmarks.

Xiangang Cheng¹, Damon Wing Kee Wong, Jiang Liu

¹Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore. xcheng@i2r.a-star.edu.sg

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This summary is machine-generated.

A novel semantic image transformation (SIT) method enhances retinal landmark detection for eye diseases. This approach accurately identifies the optic disc and macula, aiding in early diagnosis and screening.

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal landmark detection is crucial for diagnosing eye diseases like glaucoma, AMD, and diabetic retinopathy.
Automated detection systems improve screening efficiency and diagnostic accuracy.

Purpose of the Study:

To introduce a Semantic Image Transformation (SIT) approach for retinal representation and automatic landmark detection.
To develop salient models for optic disc (OD) and macula detection using SIT.

Main Methods:

The proposed SIT method analyzes local statistics of fundus images.
It enhances intrinsic retinal structures like the optic disc and macula.
Salient OD and macular models were developed based on SIT.

Main Results:

The SIT method achieved 99.44% accuracy in optic disc detection.
Macula detection accuracy reached 93.49%.
Left and right eye classification accuracy was 97.33%.

Conclusions:

The SIT approach provides accurate automatic retinal landmark detection.
This method is valuable for enhancing eye disease screening and diagnosis.
SIT improves the characterization of retinal structures for clinical applications.