MEDICAL IMAGE ENHANCEMENT USING HYBRID HONEY BADGER ONE-TO-ONE-BASED OPTIMIZATION-ENABLED ADAPTIVE HISTOGRAM EQUALIZATION

In medical science, appropriate monitoring of the function of the human body, physical representations, and accurate disease is a complex task. It helps in diagnosing and treating diseases by revealing internal structures hidden by the skin and bones. Image enhancement techniques play a crucial role in medical imaging, as they improve diagnostic accuracy and visualization. However, the conventional methods were limited by noise sensitivity, lack of adaptability, and computational complexity. Therefore, this paper proposes One-to-One Honey Optimization (OOHO) for medical image enhancement. At first, the input brain Magnetic Resonance Imaging (MRI) image is processed to find the histogram. After that, histogram clipping is performed based on the clipping threshold. Then, the exposure threshold is computed to create sub-histograms. Moreover, the Probability Density Function (PDF) is computed and updated. Likewise, the Cumulative Density Function (CDF) and the mapping function are computed in each sub-histogram. Therefore, the finest enhanced image is obtained by integrating all sub-images. The fitness value is computed utilizing the cost function. Furthermore, the optimal threshold is accomplished by utilizing OOHO, which is developed by the integration of a One-to-One-based Optimizer (OOBO) and Honey Badger optimization (HBO). The evaluation results show that the OOHO attained a Degree of Distortion (DD) of 0.270, a Pear Signal to Noise Ratio (PSNR) of 47.060 dB, a Mean Square Error (MSE) of 0.570, Structural Similarity Index Measure (SSIM) as 0.960, and Root Mean Square Error (RMSE) of 0.755. The high performance is noted by the devised model and the MSE of the OOBO, HBO, hybrid Simulated Annealing-Evaporation Rate-based Water Cycle (SA-ERWCA) algorithm, Particle Swarm Optimization combined with Histogram Equalization (PSO-HE), Genetic Algorithm-based Adaptive Histogram Equalization (GAAHE), High-Quality Guidance Network (HQG-Net), multi-scale attention generative adversarial network (MAGAN), and Gaussian Quantum-behaved Arithmetic Optimization Algorithm (GQAOA) methods are 0.657, 0.637, 0.637, 0.627, 0.670, 0.650, 0.650, and 0.640, respectively.