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Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with

Essam H Houssein1, Nada Abdalkarim1, Kashif Hussain2

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in Biology and Medicine
|January 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced Snake Optimization (SO) algorithm with opposition-based learning (OBL), termed SO-OBL, for accurate liver disease segmentation in CT scans. The SO-OBL model demonstrates superior performance and efficiency for computer-aided diagnosis systems.

Keywords:
Global optimizationImage segmentationLiver diseasesMetaheuristicsMultilevel thresholdingSnake Optimization algorithm

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Liver diseases are a major global health concern, necessitating accurate diagnostic tools.
  • Computer-aided diagnosis (CAD) systems require precise liver segmentation from CT scans for effective treatment.
  • Challenges in liver segmentation include inconsistent organ presence and ambiguous boundaries.

Purpose of the Study:

  • To develop an enhanced Snake Optimization (SO) algorithm integrated with opposition-based learning (OBL), named SO-OBL, for improved liver disease segmentation.
  • To evaluate the performance of the SO-OBL algorithm in global optimization and multilevel image segmentation tasks.
  • To create an advanced liver segmentation model for computer-aided diagnosis (CAD) systems.

Main Methods:

  • An enhanced Snake Optimization (SO) algorithm incorporating opposition-based learning (OBL) was developed (SO-OBL).
  • The SO-OBL algorithm was benchmarked against eleven state-of-the-art metaheuristic algorithms using CEC'2022 test functions.
  • A liver disease segmentation model was constructed using the SO-OBL algorithm and an optimized multilevel thresholding technique (Otsu's function).

Main Results:

  • The SO-OBL algorithm demonstrated superior performance in global optimization compared to existing metaheuristic algorithms.
  • The liver segmentation model achieved high accuracy with FSIM = 0.947, SSIM = 0.941, and PSNR = 24.876.
  • The model exhibited high efficiency with a short execution time of 0.281 seconds.

Conclusions:

  • The proposed SO-OBL algorithm effectively addresses challenges in liver segmentation from CT scans.
  • The developed segmentation model shows significant potential for accurate and efficient diagnosis in computer-aided diagnosis (CAD) systems.
  • This research contributes to advancing medical image analysis for liver disease detection.