Evidence map›Paper›PMID 35808448›Full record

ArticleSensors (Basel, Switzerland)2022

Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force.

Qianqian Qian, Ke Cheng, Wei Qian, Qingchang Deng, Yuanquan Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Qianqian QianSchool of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China.ORCID 0000-0002-2597-4085
Ke ChengSchool of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Wei QianSchool of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Qingchang DengSchool of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Yuanquan WangSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.ORCID 0000-0002-9232-5392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

Indexed as

anisotropygradient vector flowHessian matriximage structure

Identifiers

PMID35808448
PMCPMC9269761

What OpenQuestion holds

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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.