Segmentation of Retinal Blood Vessels by Top-Hat Multi-Scale Detection for Optic Disc Removal

Abstract

Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a pre-processing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the pre-processing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

“…Many researchers have worked on the automated detection of the macula using retinal fundus images. Nazari et al [19] proposed a hybrid of multi-scale detection with top-hat transformation pre-processing to enhance the contrast of the vessel from the background for blood vessel segmentation. The method achieved 95.10 % accuracy on the DRIVE dataset.…”

Glaucoma detection of retinal images based on boundary segmentation

“…For pre-processing, the method used is color channel separation method [13]. The fundus images obtained from databases are split into three color channels.…”

Retina blood vessel extraction based on kirsch’s template method

“…Subsequently, a method using the top-hat multi-scale detection proposed by Ain Nazari et al [16]. The origin of this method is proposed by Nguyen et al [17] ] but is being improved in the pre-processing phase.…”