An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation.

Yuncong Feng, Wanru Liu, Xiaoli Zhang, Zhicheng Liu, Yunfei Liu, Guishen Wang

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution.Entropy (Basel, Switzerland) · 2022
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

6 authors.

Yuncong FengCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Wanru LiuCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Xiaoli ZhangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

Zhicheng LiuCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Yunfei LiuCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Guishen WangCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.ORCID 0000-0001-6039-9285

Funding

Education Department of Jilin Province JJKH20200678KJEducation Department of Jilin Province JJKH20210752KJFundamental Research Funds for the Central Universities 93K172020K05National Natural Science Foundation of China 61801190National Natural Science Foundation of China 61876070Youth Growth Science and Technology Plan Project of Jilin Provincial Department of Science and Technology 20210508039RQ

6 · The paper itself

Abstract

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid

Indexed as

image segmentationinterval iterationlayer decompositionmultilevel thresholdingsegmentation fusion

Identifiers

PMID34828127

DOI10.3390/e23111429

PMCPMC8623348

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.