Attention-based Automated Chest CT Image Segmentation Method of COVID-19 Lung Infection

Lee, Beom J.; Martirosyan, Sarkis T.; Khan, Zaid; Chiu, Han Y.; Wang, Zun; Shi, Wenqi; Giuste, Felipe; Zhong, Yishan; Sun, Jimin; Wang, May D.

doi:10.1109/bibe55377.2022.00042

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…To conquer those problems, binary cross-entropy is used in this study to balance the number of disease and normal X-ray images and compensate for the loss function among graph mining techniques for imaging classification, clustering algorithms are extraordinarily critical, since they can categorize objects into different groups according to their similarities. Traditional clustering algorithms [4,5], such as K-means [6], are limited by their spherical assumptions, which can lead to local optima. Spectral clustering [7] has rapidly developed in recent years to compensate for these limitations by leveraging the eigenvalues of the similarity matrix.…”

Section: Introductionmentioning

confidence: 99%

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

Suresh,

Meghana,

Sujitha

et al. 2024

TURCOMAT

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Medical image segmentation plays a crucial role in various clinical applications, facilitating accurate diagnosis and treatment planning. In communication systems, particularly telemedicine and remote healthcare monitoring, real-time processing and transmission of medical images are essential for timely diagnosis and intervention. Leveraging VLSI technology for implementing Kmeans offers a promising solution to address the computational demands of image segmentation while meeting the stringent requirements of communication systems. Existing systems often rely on software-based implementations such as threshold segmentation, leading to significant computational overhead and latency, particularly in resource-constrained environments. Moreover, these implementations struggle to achieve real-time performance, hindering their practical utility in communication systems for healthcare. So, this work proposed VLSI-based approach aims to overcome these challenges by offloading the computational burden from the software to hardware, enabling parallel processing and efficient utilization of resources. By exploiting the inherent parallelism of the K-means algorithm and optimizing hardware architecture, our design ensures high throughput and low latency, making it suitable for real-time medical image segmentation in communication systems.

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