A convex selective segmentation model based on a piece-wise constant metric guided edge detector function

Khan, Muhammad Shahkar, Ali, Haider, Zakarya, Muhammad ORCID logoORCID: https://orcid.org/0000-0001-7070-6699, Tirunagari, Santosh ORCID logoORCID: https://orcid.org/0000-0002-9064-1965, Khan, Ayaz Ali, Khan, Rahim, Ahmed, Aftab and Rada, Lavdie (2022) A convex selective segmentation model based on a piece-wise constant metric guided edge detector function. Research Square . [Article] (doi:10.21203/rs.3.rs-2391118/v1)

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The challenge of segmentation for noisy images, especially those that have light in their backgrounds, is still exists in many advanced state-of-the-art segmentation models. Furthermore, it is significantly difficult to segment such images. In this article, we provide a novel variational model for the simultaneous restoration and segmentation of noisy images that have intensity inhomogeneity and high contrast background illumination and light. The suggested concept combines the multi-phase segmentation technology with the statistical approach in terms of local region knowledge and details of circular regions that are, in fact, centered at every pixel to enable in-homogeneous image restoration. The suggested model is expressed as a fuzzy set and is resolved using the multiplier alternating direction minimization approach. Through several tests and numerical simulations with plausible assumptions, we have evaluated the accuracy and resilience of the proposed approach over various kinds of real and synthesized images in the existence of intensity inhomogeneity and light in the background. Additionally, the findings are contrasted with those from cutting-edge two-phase and multi-phase methods, proving the superiority of our proposed approach for images with noise, background light, and inhomogeneity.

Item Type: Article
Sustainable Development Goals:
Research Areas: A. > School of Science and Technology
Item ID: 37162
Depositing User: Jisc Publications Router
Date Deposited: 13 Jan 2023 11:49
Last Modified: 13 Jan 2023 11:49
URI: https://eprints.mdx.ac.uk/id/eprint/37162

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