Evidence map›Paper›PMID 33204300›Full record

ArticleComputational and mathematical methods in medicine2020

Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.

Asif Aziz Memon, Shafiullah Soomro, Muhammad Tanseef Shahid, Asad Munir, Asim Niaz, Kwang Nam Choi

Abstract readComparative Study
In one paragraph

Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Asif Aziz MemonSchool of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9386-0979
Shafiullah SoomroDepartment of Computer Science, Quaid-e-Awam University of Engineering Science and Technology, Shaheed Benazirabad, Pakistan.
Muhammad Tanseef ShahidSchool of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.
Asad MunirDepartment of Industrial and Information Engineering, Università degli Studi di Udine, 33100 Udine, Italy.
Asim NiazSchool of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.
Kwang Nam ChoiSchool of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-7420-9216

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH

Indexed as

Computational BiologyComputer SimulationDatabases, FactualDeep LearningDermoscopyFemaleHumansImage Interpretation, Computer-AssistedMammographyModels, StatisticalPattern Recognition, Automated

Identifiers

PMID33204300
PMCPMC7657700

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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.