A Novel Algorithm to Analyze Retinal Image Using Morphological Operators

Santhi, N.; Shajula, G.D.; Ramar, K.

doi:10.1016/j.proeng.2012.06.446

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Rule-based methods incorporate methods for retinal blood vessel segmentation that are based on mathematical morphology, line detection, various transforms, etc. Examples of these kinds of blood vessel segmentation methods are [1], [12], [20], [22], [23], [24], [29], [34] and [35]. Nguyen et al [24] used multi-scale line detection for retinal blood vessel segmentation in order to avoid the drawbacks of the basic line detector.…”

Section: Related Workmentioning

confidence: 99%

“…Nguyen et al [24] used multi-scale line detection for retinal blood vessel segmentation in order to avoid the drawbacks of the basic line detector. Santhi et al [35] used Shearlet transform in combination with mathematical morphology, connected component analysis and length filtering in order to segment retinal blood vessels. Mendonca et al [22] used differential filters and morphological processing for retinal blood vessel segmentation.…”

Section: Related Workmentioning

confidence: 99%

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Retinal blood vessel segmentation based on heuristic image analysis

2019

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Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks -blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

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