Brain MRI/CT Contrast Enhancement using Hybrid Transformation

Objectives: To develop a better method for enhancing head MRI or CT images with the objective of suppressing irrelevant information during subsequent processing. Methods: achieve this, two innovative image enhancement techniques, ’AHE’ and ’Adaptive Gamma CDF-based Geometric Transformation,’ are combined. The proposed is evaluated using total 76 137 scan obtained from TCIA, OASIS, BCIIHM, Brain metastases datasets. tailor these methods images, background masking algorithms have been introduced in this regard. Furthermore, dark, grey, white segments histogram identified geometrically transformed into square areas, followed by gamma transformation applied to each segment. Finally, combination global AHE final enhancement. compared other state-of-the-art techniques based on evaluation parameters such as ”contrast, ”correlation, ”entropy. Findings: Both qualitative quantitative analysis exhibit how has capability than CLAHE, AGC, EGC methods. Our approach yields highest average contrast, correlation, entropy values MRI, measuring 0.522319529, 0.957321634, 5.585280467, respectively. For suggested produces maximum value, 3.472600537, 0.151943828, which just less CLAHE. correlation CT, 0.98799312, also hair EGC. These results suggest that could potentially make details structures more distinguishable. Novelty : uses 2D geometric contrast brain CT/MRI robust processes removing noise background. comparative shows exhibits superior three being compared. Keywords: Adaptive power law; CDF Transformation; Enhancement; Head Masking; Enhancement