• Axlin X
• Ajith Kumar D
• Gracy Joylet I

The implementation of a median filter in Very Large Scale Integration (VLSI) for reducing noise in brain MRI images provides a strong method to improve the clarity and accuracy of medical imaging. Brain MRI images often have salt and pepper noise, which can hide important details needed for proper analysis. In this project, the noisy MRI images are first turned into binary text format and stored in RAM. This pre-processing makes it easier to handle and process the data within the VLSI system. The median filter module works by using a buffer to hold the incoming data and then sorting this data to find the median value, which helps remove the noise. By applying a double median filtering process to the stored data, noise is greatly reduced while keeping important image details. A line buffer is used to store temporary values, making the filtering process efficient and fast enough for real-time use. This method not only improves image quality but also uses the parallel processing power of VLSI to work quickly. A detailed test bench checks the RAM and median filter modules, showing the steps of reading binary data from an input file, filtering it, and writing the cleaned output to a new file. The resulting images have less noise and are clearer, making them better for medical analysis and interpretation. This project shows how VLSI technology can improve medical imaging techniques.

VLSI, RAM , Median filter, MRI images

"Implementation of a Double Median Filter in VlSI for Noise Reduction in Brain MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d808-d821, September-2024, Available :http://www.jetir.org/papers/JETIR2409393.pdf

"Implementation of a Double Median Filter in VlSI for Noise Reduction in Brain MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd808-d821, September-2024, Available at : http://www.jetir.org/papers/JETIR2409393.pdf