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Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection

Anusree Kanadath1, J Angel Arul Jothi1, Siddhaling Urolagin1

  • 1Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai International Academic City, Dubai P.O. Box 345055, United Arab Emirates.

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Summary

This study introduces a novel Multilevel Multiobjective Particle Swarm Optimization guided Superpixel (MMPSO-S) algorithm for segmenting histopathology images. The MMPSO-S algorithm demonstrates superior performance in detecting regions of interest compared to existing methods.

Keywords:
histopathologyimage segmentationmultiobjective algorithmsnature-inspired algorithmsparticle swarm optimizationthresholding

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Area of Science:

  • Medical image analysis
  • Computational intelligence
  • Digital pathology

Background:

  • Histopathology image analysis is crucial for early disease diagnosis, particularly cancer.
  • Computer-aided diagnosis (CAD) algorithms have advanced histopathology image segmentation.
  • Swarm intelligence applications in histopathology image segmentation remain underexplored.

Purpose of the Study:

  • To introduce a novel algorithm, Multilevel Multiobjective Particle Swarm Optimization guided Superpixel (MMPSO-S), for histopathology image segmentation.
  • To effectively detect and segment regions of interest (ROIs) in Hematoxylin and Eosin (H&E)-stained images.
  • To evaluate the performance of the proposed MMPSO-S algorithm on diverse histopathology datasets.

Main Methods:

  • Development of the Multilevel Multiobjective Particle Swarm Optimization guided Superpixel (MMPSO-S) algorithm.
  • Application of the algorithm to segment ROIs in H&E-stained histopathology images.
  • Experimental validation on TNBC, MoNuSeg, MoNuSAC, and LD datasets.

Main Results:

  • The MMPSO-S algorithm achieved high performance metrics across datasets, including a Jaccard coefficient of 0.56 and Dice coefficient of 0.72 on MoNuSeg.
  • For the LD dataset, the algorithm obtained a precision of 0.96, recall of 0.99, and F-measure of 0.98.
  • Comparative analysis showed the superiority of MMPSO-S over standard PSO, its variants, MOEA/D, NSGA2, and traditional image processing methods.

Conclusions:

  • The proposed MMPSO-S algorithm offers an effective approach for histopathology image segmentation.
  • MMPSO-S outperforms existing swarm intelligence and traditional methods in detecting and segmenting ROIs.
  • This advancement holds promise for improving computer-aided diagnosis in digital pathology.