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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A proposed scenario to improve the Ncut algorithm in segmentation.

Nhu Y Tran1,2, Huynh Trung Hieu1, Pham The Bao3

  • 1Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam.

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|March 20, 2023
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Summary
This summary is machine-generated.

This study introduces an automated method for determining the number of clusters (k) in image segmentation using histogram data. This approach enhances the Normalized Cut (Ncut) algorithm, significantly improving speed without sacrificing accuracy.

Keywords:
CPUFCMGPUNcutparallel computing

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation typically requires pre-defining the number of clusters (k).
  • Existing methods rely on external knowledge or observation to set 'k'.
  • This limitation hinders the automation and adaptability of image segmentation.

Purpose of the Study:

  • To propose a novel scenario for automatically determining the number of clusters (k) in image segmentation.
  • To improve the efficiency and applicability of the Normalized Cut (Ncut) algorithm.
  • To leverage histogram information for automated cluster number determination.

Main Methods:

  • Implemented an automated cluster number determination method using histogram analysis.
  • Integrated this method into the Ncut algorithm.
  • Utilized CUDA for parallel computing on GPUs to accelerate Ncut.
  • Incorporated Fuzzy C-Means (FCM) clustering for the grouping stage.

Main Results:

  • The proposed scenario automatically determines the optimal number of clusters (k).
  • The enhanced Ncut algorithm demonstrates a 20x speedup compared to the standard Ncut.
  • The accuracy of image segmentation is maintained at levels comparable to the original Ncut algorithm.
  • GPU acceleration via CUDA significantly reduces processing time.

Conclusions:

  • The novel scenario effectively automates cluster number determination in image segmentation.
  • The integration with Ncut and GPU acceleration offers a substantial performance improvement.
  • This approach provides a faster and more automated solution for image segmentation tasks.