An Effective Segmentation of MRI Images Combining Threshold and Hybrid Particle Swarm Optimization (HPSO-T) for Lung, Bone and Brain (LBB)
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 12 |
| Year of Publication : 2024 |
| Authors : N. Raghapriya, N. Aswini, G. Savitha |
10.14445/23488549/IJECE-V11I12P109
How to Cite?
N. Raghapriya, N. Aswini, G. Savitha, "An Effective Segmentation of MRI Images Combining Threshold and Hybrid Particle Swarm Optimization (HPSO-T) for Lung, Bone and Brain (LBB)," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 12, pp. 92-99, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P109
Abstract:
Segmentation in medical imaging is one of the fundamental problems in image processing. Perceptual completion and recognition during picture segmentation are the issues with image segmentation. Machine vision-based image threshold segmentation is an essential detecting tool. The issue of time consumption arises with the traditional threshold picture segmentation method. However, optimization techniques can help to resolve these problems. An effective optimization technique is needed to determine the ideal threshold. The thresholding will become more computationally intensive with increasing thresholds. This research proposed Hybrid Particle Swarm Optimization with Thresholding (HPSO-T) technique used for image segmentation to assess the MRI medical Image for detecting and managing various tumors in Lung, Brain and Bone-(LBB). This work extracts the MRI scan pictures using the LBB data acquired from the Kaggle website. The suggested segmentation methodology outperforms the other two segmentation approaches in the market with a Dice Index of 0.93.
Keywords:
Segmentation, MRI images, LBB, HPSO-T, Optimization.
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