FPGA Accelerated Gradient Analysis for Brain Tumours Characterization

Real-time image processing demands rapid analysis of extensive data. This system for tumour characterisation uses high-resolution MRI datasets. The system leverages the parallel processing capabilities of FPGAs to perform real-time edge detection using a modified Sobel operator. A novel self-checking test bench was developed to validate the accuracy of FPGA results against C program results, ensuring robust performance. The proposed method was tested on a diverse set of tumour images, demonstrating enhanced edge clarity and reduced noise. This work contributes to the advancement of biomedical imaging, offering a scalable and efficient solution for real-time tumour characterization. Future work will explore the integration of machine learning techniques and adaptation to other imaging modalities.