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research-article

Active contour model based on local Kullback–Leibler divergence for fast image segmentation

Authors:

Yiyang ChenAuthors Info & Claims

Volume 123, Issue PC

https://doi.org/10.1016/j.engappai.2023.106472

Published: 01 August 2023 Publication History

Abstract

The inhomogeneity of image intensity and noise are the main factors that affect the segmentation results. To overcome these challenges, a new active contour model is designed based on level set method and Kullback–Leibler Divergence. First of all, a new regional measurement of information scale is applied to construct energy functional, instead of Euclidean distance. Test results demonstrate that the Kullback–Leibler Divergence achieves a truly better segmentation. Then, a new Heaviside function has been proposed in this paper, which gives rise to a faster zero-crossing slope than traditional function. In this sense, it can stimulate the evolution of the level set function faster and allocate internal and external energy reasonably. In addition, the activation function has also been improved, which makes itself fluctuates over a smaller range than former activation function. Experiments reveal that the ‘Local Kullback–Leibler Divergency’ (LKLD) model has desired segmentation results both on real-world and medical images. Also, it owns a better noise robustness and is not limited to position of initial contour.

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Cited By

Ge PChen YWang GWeng G(2024)An active contour model based on Jeffreys divergence and clustering technology for image segmentationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.10406999:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.jvcir.2024.104069
Sun JFeng QZhang YRu MLi YLi TZhao C(2024)Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robotComputers and Electronics in Agriculture10.1016/j.compag.2024.109332225:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109332

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Active contour model based on local Kullback–Leibler divergence for fast image segmentation
1. Computing methodologies
2. Mathematics of computing

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Published In

cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 123, Issue PC

Aug 2023

1268 pages

ISSN:0952-1976

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 August 2023

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Cited By

Ge PChen YWang GWeng G(2024)An active contour model based on Jeffreys divergence and clustering technology for image segmentationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.10406999:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.jvcir.2024.104069
Sun JFeng QZhang YRu MLi YLi TZhao C(2024)Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robotComputers and Electronics in Agriculture10.1016/j.compag.2024.109332225:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109332

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