An Improved Combined Adaptive Outline for Contrast Enhancement of Blood Vessels
subject: Adaptive histogram equalization (AHE); Brightness preserving bi-histogram equalization (BBHE); Contrast enhancement; Histogram equalization (HE)
description: Appropriate vascular segmentation is dependent on effective picture pre-processing techniques that improve the contrast of the blood vessels, reduce noise, eliminate non-uniform illumination, highlight thin vessels, and retain background texture. These techniques are necessary for accurate vessel segmentation. Here, both the edge- and texture-smoothed data from the vessel probability map are used in the derivation of the adaptive optimal q-order in the G-L mask. The smooth information is not affected, the textures are maintained, and the contrast of the blood vessels is enhanced, thanks to the proposed filter. In addition to sharpening the focus on the vessels themselves, a Gaussian curve fitting is used to contrast stretch the entire image. Retinal fundus images processed with cerebral DSA are subjected to both qualitative and quantitative assessments of contrast enhancement. Quantitative performance indicators are tabulated and compared to other approaches to show how well this technique works for improving medical images everywhere. The suggested filter is easy to implement, flexible enough to adapt to different images, and effective at increasing both vessel contrast and overall image contrast. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
creator: Warrier G.S.; Mahapatra H.; Khuntia M.; Rajesh M.; Vincent R.; Pathak A.
source: SN Computer Science, Vol-4, No. 5
publisher: Springer
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 2662995X
format: Online
language: English
type: Article
coverage: Warrier G.S., Department of Computer Science, Christ(Deemed to be University), Karnataka, Bangalore, 560 073, India; Mahapatra H., Department of Endocrinology, IMS & SUM Hospital, SOA University, Bhubaneswar, India, Sevayan Diabetes Centre, Puri, India; Khuntia M., Department of Rural Management, KIIT University, Bhubaneswar, India; Rajesh M., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India; Vincent R., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India; Pathak A., Department of Computer Science, Shobhit Institute of Engineering & Technology (Deemed to-be University), Meerut, India