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

Article


Khan, M., Ali, H., Zakarya, M., Tirunagari, S., Khan, A., Khan, R., Ahmed, A. and Rada, L. 2022. A convex selective segmentation model based on a piece-wise constant metric guided edge detector function. Research Square. https://doi.org/10.21203/rs.3.rs-2391118/v1
TypeArticle
TitleA convex selective segmentation model based on a piece-wise constant metric guided edge detector function
AuthorsKhan, M., Ali, H., Zakarya, M., Tirunagari, S., Khan, A., Khan, R., Ahmed, A. and Rada, L.
Abstract

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.

Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherPrePrint, Research Square Platform LLC
JournalResearch Square
Publication dates
Online29 Dec 2022
Publication process dates
Deposited13 Jan 2023
Submitted20 Dec 2022
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.21203/rs.3.rs-2391118/v1
LanguageEnglish
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