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Related Concept Videos

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: Sep 20, 2025

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Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical

Hang Su1, Dong Zhao1, Hela Elmannai2

  • 1College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.

Computers in Biology and Medicine
|June 11, 2022
PubMed
Summary

A new multilevel thresholding image segmentation (MTIS) method using an enhanced multiverse optimizer (CCMVO) improves COVID-19 chest radiography analysis. This AI-driven approach offers more efficient and accurate segmentation for diagnosing coronavirus pneumonia.

Keywords:
COVID-19Meta-heuristicMulti-threshold image segmentationMulti-verse optimizationNovel coronavirus pneumoniaOptimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • COVID-19 diagnosis relies heavily on chest radiography analysis.
  • Manual processing of radiographic images is time-consuming and prone to inaccuracies.
  • Efficient and accurate automated methods are crucial for timely COVID-19 diagnosis.

Purpose of the Study:

  • To develop an efficient and accurate automated method for segmenting COVID-19 chest radiographs.
  • To introduce an enhanced multiverse optimizer (CCMVO) for improved image segmentation.
  • To evaluate the performance of the proposed method in segmenting COVID-19 affected lung images.

Main Methods:

  • Proposed a multilevel thresholding image segmentation (MTIS) method.
  • Enhanced the Multi-Verse Optimizer (MVO) by incorporating horizontal and vertical search mechanisms, creating the CCMVO algorithm.
  • Validated CCMVO against benchmark functions and other optimization algorithms (DE, MVO, HHO, SCA).

Main Results:

  • The CCMVO algorithm demonstrated superior global search capability and avoidance of local optima compared to other algorithms.
  • The CCMVO-based MTIS method achieved higher quality segmentation results for COVID-19 chest radiographs.
  • Quantitative evaluation using FSIM, PSNR, and SSIM confirmed the effectiveness of the proposed segmentation technique.

Conclusions:

  • The proposed CCMVO-based MTIS method provides an effective solution for processing COVID-19 chest radiography.
  • This approach can significantly aid medical professionals in the accurate and efficient diagnosis of coronavirus pneumonia.
  • The enhanced optimization algorithm shows promise for applications in medical image analysis and diagnostics.