ANALYSIS OF BIOMEDICAL FILTER PARAMETERS USING CT IMAGES

Abstract

Abstract

The analysis and enhancement of biomedical images are crucial for accurate diagnosis and treatment planning. This study focuses on the evaluation of biomedical filter parameters using CT images, emphasizing the Gamma correction method and Homomorphic filtering. Gamma correction is utilized to adjust image luminance, thereby enhancing contrast and improving the visibility of critical anatomical structures. By varying the gamma values, we tailored the image quality to specific diagnostic needs, such as distinguishing between soft tissues and bone structures. Homomorphic filtering, meanwhile, addresses the dual challenge of contrast enhancement and noise reduction by transforming the image into the logarithmic domain and separating illumination from reflectance. This method significantly improved image clarity and consistency by standardizing lighting conditions. Our comparative analysis demonstrates that both Gamma correction and Homomorphic filtering offer substantial benefits for biomedical image enhancement, each with unique strengths. Gamma correction provides flexible, user-defined contrast adjustments, while Homomorphic filtering excels in mitigating lighting inconsistencies and noise. The findings highlight the potential for these techniques to enhance diagnostic accuracy and suggest avenues for further research into their integration and optimization in automated imaging systems.