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
Type | Article |
---|---|
Title | A convex selective segmentation model based on a piece-wise constant metric guided edge detector function |
Authors | Khan, 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 Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Publisher | PrePrint, Research Square Platform LLC |
Journal | Research Square |
Publication dates | |
Online | 29 Dec 2022 |
Publication process dates | |
Deposited | 13 Jan 2023 |
Submitted | 20 Dec 2022 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.21203/rs.3.rs-2391118/v1 |
Language | English |
File | License |
https://repository.mdx.ac.uk/item/8q381
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