Evidence map›Paper›PMID 34821859›Full record

ArticleJournal of imaging2021

On a Variational and Convex Model of the Blake-Zisserman Type for Segmentation of Low-Contrast and Piecewise Smooth Images.

Liam Burrows, Anis Theljani, Ke Chen

Abstract read
In one paragraph

Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Liam BurrowsLiverpool Centre of Mathematics for Healthcare and Centre for Mathematical Imaging Techniques, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.ORCID 0000-0002-6910-6693
Anis TheljaniLiverpool Centre of Mathematics for Healthcare and Centre for Mathematical Imaging Techniques, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.ORCID 0000-0002-9126-2158
Ke ChenLiverpool Centre of Mathematics for Healthcare and Centre for Mathematical Imaging Techniques, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.ORCID 0000-0002-6093-6623

Funding

Engineering and Physical Sciences Research Council EP/N014499/1
6 · The paper itself

Abstract

This paper proposes a new variational model for segmentation of low-contrast and piecewise smooth images. The model is motivated by the two-stage image segmentation work of Cai-Chan-Zeng (2013) for the Mumford-Shah model. To deal with low-contrast images more effectively, especially in treating higher-order discontinuities, we follow the idea of the Blake-Zisserman model instead of the Mumford-Shah. Two practical ideas are introduced here: first, a convex relaxation idea is used to derive an implementable formulation, and second, a game reformulation is proposed to reduce the strong dependence of coupling parameters. The proposed model is then analysed for existence and further solved by an ADMM solver. Numerical experiments can show that the new model outperforms the current state-of-the-art models for some challenging and low-contrast images.

Indexed as

Blake–Zissermangame theoryimage segmentationMumford–Shah

Identifiers

PMID34821859
PMCPMC8621176

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.