For example, the thickness of the retina, especially the nerve fiber layer (NFL), has been used to indicate the progression of glaucoma 1. The structural features of retina have been shown to be closely related to many ophthalmological diseases. It is easy to understand and improve, and may become a useful tool for analyzing and diagnosing eye diseases. This algorithm can be used alone or in combination with other methods as an initial boundary detection algorithm. The percentage of “perfect segmentation” and “good segmentation” is 98% in healthy subjects and 94% in AMD patients. Qualitative method is also used to verify the accuracy and stability of the algorithm.
The average difference between the automatic and manual methods is: 2–6 microns (1–2 pixels) for healthy subjects and 3–10 microns (1–3 pixels) for AMD patients.
The algorithm was used to segment the retinal images of healthy subjects and age-related macular degeneration (AMD) patients eleven retinal boundaries were identified and compared with the results of manual segmentation by the ophthalmologists. The improved algorithm solves the problems of the traditional Canny operator by adding a multi-point boundary search step on the basis of the original method, and adjusts the convolution kernel. This paper proposed an improved Canny operator for automatic segmentation of retinal boundaries. However, the traditional Canny operator is susceptible to vascular shadows, vitreous artifacts, or noise interference in retinal segmentation, causing serious misdetection or missed detection. Canny operator is recognized as the best boundary detection operator so far, and is often used to obtain the initial boundary of the retina in retinal segmentation.
Retinal segmentation is a prerequisite for quantifying retinal structural features and diagnosing related ophthalmic diseases.