VLSI-Driven Chest X-Ray Segmentation: Hybrid Clustering Approach for Enhanced Medical Image Processing
DOI:
https://doi.org/10.48047/Keywords:
Hybrid Clustering, Image Segmentation, Pixel Analysis, Chest x ray images, Xilinx.Abstract
In many clinical applications, medical picture segmentation is essential for precise diagnosis and therapy planning. For prompt diagnosis and intervention, real-time processing and transmission of medical pictures is crucial in communication systems, especially in telemedicine and remote healthcare monitoring. Utilizing VLSI technology to implement K-means presents a viable way to satisfy the strict criteria of communication systems while addressing the computational demands of picture segmentation. Current systems frequently depend on software-based techniques, like threshold segmentation, which can result in a large amount of processing overhead and latency, especially in contexts with limited resources.
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References
. Song, Jiang, Yuan Gu, and Ela Kumar. "Chest sdisease image classification based on spectral clustering algorithm." Research Reports on Computer Science (2023): 77-90.
. Agrawal, Tarun, and Prakash Choudhary. "Segmentation and classification on chest radiography: a systematic survey." The Visual Computer 39, no. 3 (2023): 875-913.
. Chakraborty, Gouri Shankar, Salil Batra, and Makul Mahajan. "A Novel Deep Learning-based Approach for Covid-19 Infection Identification in Chest X-ray Image using Improved Image Segmentation Technique." In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1102-1109. IEEE, 2023.
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