Objective

2

The macula in the retinal fundus is an oval-shaped pigmented area near the center of the retina and is responsible for detailed central vision

which is needed in activities such as reading and driving. The fovea is located at the center of the macula

which is the site of the sharpest center vision. Any abnormal changes in the macula may cause vision loss. The distance between the lesions and fovea has clinical relevance to the extent of damage. Therefore

accurate localization of the fovea has an important significance in computer-aided diagnosis of diabetic retinopathy. However

fovea detection faces challenges that vary illumination

computational load

and abnormal images. This study develops a novel fovea detection approach by using blood vessels projection and mathematical morphology to achieve accurate and effective fovea detection. A series of techniques has been proposed for fovea detection using color retinal images

which can be divided into anatomical-based and visual-based features. The former criterion involves techniques to determine the region of interest (ROI) that contains fovea by using the geometrical relationship between the fovea and the optic disc diameter. The fovea can be located at a distance that is approximately two and a half disc diameters from the optic disc center. The latter criterion includes techniques of searching the fovea together with visual features of the fovea

such as a round-shaped dark area in the neighborhood of the optic disc.

Method

2

Our proposed fovea detection approach consists of four steps. First

we use a series of mathematical morphology techniques that contain open

erosion

and reconstruction operations for automatic blood vessel detection. The center of the optic disc is then determined using the primary distribution of blood vessels within the optic disc (vertical vessel is aggregated

and few blood vessels are present above or below the optic disc). The anatomical prior information between the optic disc and the macula indicates that the fovea can be located approximately two and a half disc diameters from the optic disc center. Thus

an ROI that contains the fovea is extracted. Finally

we conduct fovea localization on the green channel of the corresponding ROI image that contains the fovea. Brightness adjustment is first applied to improve the contrast of the fovea within the ROI to make the fovea region highly visible. Morphological erosion reconstruction operation is used to remove the lesions

which have a similar color as the fovea. The macula has low luminance. Thus

we invert the reconstructed image and extract the binary image using region maximum and Ostu threshold techniques. Dilatation operation is used to smooth the extracted candidate regions. A series of features of candidate regions is extracted to determine the fovea.

Result

2

Two standard diabetic retinopathy databases

DIARETDB0 and DIARETDB1

are used to evaluate the effectiveness of the proposed approach. The proposed approach achieves 96.92% and 96.63% success rates on DIARETDB0 and DIARETDB1

respectively

with a total success rate of 96.35%

thereby outperforming state-of-the-art fovea detection approaches. Experimental results indicate that the locations of the macula fovea detected by using our proposed method are close to the corresponding reference positions marked by specialists. Moreover

the average execution time on DIARETDB0 and DIARETDB1 is 8.236 s and 8.912 s

respectively.

Conclusion

2

Experimental results indicate that the proposed approach can locate macula fovea quickly and accurately

and performs better than existing macula fovea detection approaches.