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Enhancing interdisciplinary image segmentation through a Gaussian-based modified local consensus spatial fuzzy

Srirupa Das1, Kamarujjaman2, Suchismita Dhar3

  • 1RCC Institute of Information Technology, Kolkata, India.

Computers in Biology and Medicine
|March 22, 2025
PubMed
Summary

A new Gaussian-based Modified Local Consensus Spatial Fuzzy (GMLCSF) approach effectively classifies diverse images, even with noise and artifacts. This method enhances accuracy for satellite and medical imaging, outperforming existing techniques.

Keywords:
ArtifactsBrain MRI imageClassificationFuzzy C-MeansGaussian-basedIntensity inhomogeneityLocal consensusRemote sensingSegmentationSpatial information

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Area of Science:

  • Image Processing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Image classification faces challenges with diverse data sources, varying resolutions, and inherent artifacts.
  • Traditional and advanced fuzzy-based methods struggle with datasets containing multiple imaging sources and uncertainties.
  • Accurate classification of visual data is critical for applications like remote sensing and medical imaging.

Purpose of the Study:

  • To introduce a generic fuzzy-based approach, Gaussian-based Modified Local Consensus Spatial Fuzzy (GMLCSF), for robust image classification.
  • To address limitations of existing methods when dealing with diverse image sources, inhomogeneities, and artifacts.
  • To enhance the accuracy and efficacy of image classification frameworks irrespective of data origin and uncertainties.

Main Methods:

  • Utilizes a histogram peak associative rule for intelligent cluster identification and center initialization.
  • Incorporates a consensus-inspired local spatial membership function with a global function to mitigate noise and inhomogeneities.
  • Formulates Gaussian, geometric, and local consensus-based spatial information for improved classification accuracy.

Main Results:

  • Demonstrates superior performance over state-of-the-art techniques on remote sensing and MRI datasets.
  • Quantitative evaluation using partition coefficient, entropy, and spectral angle distance confirms effectiveness.
  • Qualitative analysis of classified images reveals significant improvements in accuracy and noise reduction.

Conclusions:

  • The proposed GMLCSF approach offers a powerful and versatile solution for image classification across diverse datasets.
  • GMLCSF effectively handles uncertainties, noise, and artifacts, outperforming existing methods.
  • This framework provides a significant advancement for applications requiring high-accuracy image classification.