Open Access

ARTICLE

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Sonali Dash¹, Sahil Verma^2,*, Kavita², N. Z. Jhanjhi³, Mehedi Masud⁴, Mohammed Baz⁵
1 Department of Electronics and Communication Engineering, Raghu Institute of Technology (A), Visakhapatnam, 531162, India
2 Department of Computer Science and Engineering, Chandigarh University, Mohali, 140413, India
3 School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya Malaysia
4 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif, 21994, Saudi Arabia
* Corresponding Author: Sahil Verma. Email:

Computers, Materials & Continua 2022, 71(2), 2459-2476. https://doi.org/10.32604/cmc.2022.020904

Received 13 June 2021; Accepted 24 September 2021; Issue published 07 December 2021

Abstract

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.

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Keywords

Blood vessel extraction; curvelet transform; fast bilateral filter; C mean thresholding

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