A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity



Ali, Haider, Badshah, Noor, Chen, Ke ORCID: 0000-0002-6093-6623 and Khan, Gulzar Ali
(2016) A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity. PATTERN RECOGNITION, 51. pp. 27-42.

[img] Text
Novel_V6.pdf - Unspecified
Access to this file is embargoed until Unspecified.

Download (4MB)

Abstract

Level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects/regions in a given image, provided that the underlying intensity has homogeneity. The case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well-known work of Chan-Vese (2001) and its many variants. However for images with intensity inhomogeneity or multiphases within the foreground of objects, such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful. To deal with such problems, in this paper, we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images. Tests and comparisons will show that our new model outperforms two previous models. Both synthetic and real life images are used to illustrate the reliability and advantages of our new model.

Item Type: Article
Uncontrolled Keywords: Image segmentation, Calculus of variations, Level set method, Partial differential equations, Edges, Objects
Depositing User: Symplectic Admin
Date Deposited: 07 Sep 2015 08:18
Last Modified: 15 Dec 2022 12:06
DOI: 10.1016/j.patcog.2015.08.022
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/2024299