A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance

Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all o...

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Published in:IEEE access 2022, Vol.10, p.22344-22358
Main Authors: Rahman, Afzal, Ali, Haider, Badshah, Noor, Rada, Lavdie, Khan, Ayaz Ali, Hussain, Hameed, Zakarya, Muhammad, Ahmed, Aftab, Rahman, Izaz Ur, Raza, Mushtaq, Haleem, Muhammad
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Language:English
Subjects:
Active contours
Background noise
Computational modeling
Edge detection
Euler-Lagrange equation
Homogeneity
Image edge detection
Image segmentation
Level set
level set function
local similarity factor
local spatial distance
Mathematical models
Medical imaging
Motion segmentation
Robustness
selective segmentation
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cited_by cdi_FETCH-LOGICAL-c408t-17f346ef5bafee82d2ac51fa52b284b598643f5e9f789da9b4506f02bafba873
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container_title IEEE access
container_volume 10
creator Rahman, Afzal
Ali, Haider
Badshah, Noor
Rada, Lavdie
Khan, Ayaz Ali
Hussain, Hameed
Zakarya, Muhammad
Ahmed, Aftab
Rahman, Izaz Ur
Raza, Mushtaq
Haleem, Muhammad
description Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.
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source IEEE Xplore Open Access Journals
subjects Active contours
Background noise
Computational modeling
Edge detection
Euler-Lagrange equation
Homogeneity
Image edge detection
Image segmentation
Level set
level set function
local similarity factor
local spatial distance
Mathematical models
Medical imaging
Motion segmentation
Robustness
selective segmentation
title A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance
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