Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques

By Arturo Aquino, Manuel Emilio Gegúndez-Arias, Diego Marín. IEEE Transactions on Medical Imaging, 2010

559 citations recorded by the source corpus.

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a23a675ada53522ddc2796a758be8752edc324d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Kang Zhou
2. Shenghua Gao
3. Huan Fu
4. Yitian Zhao
5. Jiang Liu
6. Zaiwang Gu
7. Huaying Hao
8. Tianyang Zhang

In Ophthalmology

1. Huan Fu
2. Jiang Liu
3. Jun Cheng

In Radiology, Nuclear Medicine and Imaging

1. Huan Fu
2. Jiang Liu
3. Jun Cheng

Citations in the corpus

• CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “MEDICAL image segmentation is often an important step in medical image analysis, such as optic disc segmentation [1]–[3] and blood vessel detection [4]–[8] in retinal images, cell segmentation [9]–[11] in electron microscopic (EM) recordings, lung segmentation [12]–[16] and brain segmentation [17]–[”
• Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018, unknown): “In [20] and [21], the Circular-based Transformation”