FILTER BASED ANALYSIS FOR THE MRI DATA IN BIOMEDICAL ENGINEERING

Abstract

Abstract

In biomedical engineering, the enhancement of MRI data is pivotal for precise diagnosis and effective treatment planning. This study presents a filter-based analysis of MRI data, focusing on the application and efficacy of Homomorphic filtering and Gamma filtering techniques. Homomorphic filtering is employed to address the challenges of contrast enhancement and noise reduction by transforming the image into the logarithmic domain, thereby separating the illumination and reflectance components. This approach significantly improves image clarity and consistency, making it easier to identify and analyze anatomical structures. Gamma filtering, on the other hand, adjusts the luminance of MRI images to enhance contrast and visibility of specific features. By optimizing gamma values, we tailored the image quality to highlight different tissue types and pathological conditions. Our comparative analysis reveals that both Homomorphic filters and Gamma filtering offer substantial improvements in the quality of MRI data, each with distinct advantages. Homomorphic filtering excels in noise reduction and standardizing lighting conditions, while Gamma filtering provides flexible contrast enhancement tailored to diagnostic needs. The findings underscore the potential of these techniques to advance the accuracy and reliability of MRI-based diagnostics in biomedical engineering, suggesting further research into their combined application and optimization for automated imaging systems.