Abstract
Medical image processing plays an indispensable role in diagnosing and treating brain tumors. The objective of this study was to introduce an innovative hardware architecture designed to enhance the accuracy of segmenting brain tumors in 3D MRI images. The proposed approach combines intelligent algorithms, specifically particle swarm optimization and Darwin particle swarm optimization, to achieve high precision in tumor segmentation. We implemented this method on a global framework for Xilinx Virtex6 FPGA. The performance and robustness of the model were examined using two distinct datasets: BRATS 2021 and BRATS 2013. Experimental findings underscored the model's outstanding efficacy. The results indicated that our hardware architecture delivered robust and efficient performance in segmenting brain tumors. The introduced hardware architecture offers significant potential in aiding clinicians to diagnose patients and determine lesion extents. By employing advanced algorithms and hardware designs, this method promises to boost the speed and precision of brain tumor diagnosis and treatment. The study thus contributes an invaluable tool for healthcare professionals in the realm of brain tumor detection and management.
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Datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Gtifa, W., Sakly, A. Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging. J Real-Time Image Proc 20, 115 (2023). https://doi.org/10.1007/s11554-023-01372-x
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DOI: https://doi.org/10.1007/s11554-023-01372-x