Background:
The most important aspect of medical image processing and analysis is
image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of
features, and even higher-level procedures. The problem with image segmentation is recognition
and perceptual completion while segmenting the image. However, these issues can be resolved
by multilevel optimization techniques. However, multilevel thresholding will become more
computationally intensive with increasing thresholds. Optimization algorithms can resolve these
issues. Therefore, hybrid optimization is used for image segmentation in this research work.
Methods:
The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective
function in the nature-inspired optimization algorithm.
Results:
The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio
(PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS
datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency.
Conclusion:
The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical
image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.
