Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging

Gtifa, Wafa; Sakly, Anis

doi:10.1007/s11554-023-01372-x

J Real-Time Image Proc

2023

DOI: 10.1007/s11554-023-01372-x

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Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging

Wafa Gtifa,

Anis Sakly

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2024

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Cited by 2 publications

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AM YOLO: adaptive multi-scale YOLO for ship instance segmentation

Yuan,

Meng,

2024

J Real-Time Image Proc

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AM YOLO: adaptive multi-scale YOLO for ship instance segmentation

Yuan,

Meng,

2024

J Real-Time Image Proc

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VLSI implementation of a modified min-max median filter using an area and power competent tritonic sorter for image denoising

Christudhas,

Fathima

2024

Sci Rep

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The prominence of image processing in today’s cutting-edge technology is undeniable. Integrating software with hardware leverages both strengths, resulting in a real-time processing system that is efficient and streamlined. Raw images are usually affected by noise, which hinders the acquisition of good-quality and detailed images; hence, denoising becomes necessary. This paper proposes a modified min-max median (MMM) filter to remove impulse noise and a Tritonic sorter to localize corrupted pixels. The proposed denoising method focuses on localizing noisy pixels, unlike traditional denoising approaches, which focus only on noise detection and filtering. A min-max sheet provides the location of the corrupted pixels, and filtering is performed on them. The Tritonic Sorter, consisting of a max locator and a min locator, compares three input values and finds the minimum, maximum and median values among them. Compared to other state-of-the-art methods, the proposed method minimizes the number of comparators needed to carry out the sorting process. The proposed method was synthesized in the ZedBoard Zynq kit using the Vivado tool. The results show that the area improved by 27%, and the power improved by 16.23% compared with those of the existing method.

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Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging

Cited by 2 publications

References 17 publications

AM YOLO: adaptive multi-scale YOLO for ship instance segmentation

AM YOLO: adaptive multi-scale YOLO for ship instance segmentation

VLSI implementation of a modified min-max median filter using an area and power competent tritonic sorter for image denoising

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