• Anindya Mondal

Medical imaging plays a crucial role in diagnosis and treatment planning, where the quality of images directly impacts the accuracy of medical assessments. This research focuses on advancing image quality in medical imaging applications through the integration of adaptive noise reduction and edge-preserving filtering techniques. The proposed methodology leverages sophisticated algorithms to address challenges associated with noise reduction while preserving essential image details, particularly along edges critical for diagnostic interpretation. The first component of the approach involves an adaptive noise reduction algorithm tailored for medical images. Traditional noise reduction methods may inadvertently compromise image clarity, especially in low-dose or low-contrast scenarios. The adaptive nature of the proposed algorithm ensures that noise reduction is dynamically adjusted based on the specific characteristics of the image, optimizing the balance between noise suppression and preservation of diagnostic features. The second key aspect of the research focuses on edge-preserving filtering techniques to enhance image sharpness and maintain structural details. By incorporating advanced filtering methods, the proposed framework aims to prevent blurring along edges, which is essential for accurate interpretation in medical contexts. This involves the development of algorithms that selectively enhance or smooth pixel intensities based on the local image structure, thereby preserving crucial anatomical information. To validate the effectiveness of the proposed methodology, extensive experiments are conducted using diverse medical imaging modalities, such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI). Quantitative metrics, including signal-to-noise ratio (SNR) and edge preservation indices, are employed to assess the performance of the proposed approach compared to existing methods. The results demonstrate notable improvements in image quality, highlighting the potential impact on diagnostic accuracy and clinical decision-making. In conclusion, this research contributes to the field of medical imaging by presenting a comprehensive framework for enhancing image quality through adaptive noise reduction and edge-preserving filtering techniques. The proposed methodology addresses the intricate balance between noise reduction and preservation of critical image features, offering a valuable tool for medical professionals in their diagnostic endeavors.

ENHANCING IMAGE QUALITY THROUGH ADAPTIVE NOISE REDUCTION AND EDGE-PRESERVING FILTERING TECHNIQUES IN MEDICAL IMAGING APPLICATIONS

"ENHANCING IMAGE QUALITY THROUGH ADAPTIVE NOISE REDUCTION AND EDGE-PRESERVING FILTERING TECHNIQUES IN MEDICAL IMAGING APPLICATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.g32-g37, January-2022, Available :http://www.jetir.org/papers/JETIR2201609.pdf

"ENHANCING IMAGE QUALITY THROUGH ADAPTIVE NOISE REDUCTION AND EDGE-PRESERVING FILTERING TECHNIQUES IN MEDICAL IMAGING APPLICATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppg32-g37, January-2022, Available at : http://www.jetir.org/papers/JETIR2201609.pdf