Bedsides diagnosis using portable ultrasound scanning (PUS) offering comfortable diagnosis with various clinical advantages, in general, ultrasound scanners suffer from a poor signal-to-noise ratio, and physicians who operate the device at point-of-care may not be adequately trained to perform high level diagnosis. Such scenarios can be eradicated by incorporating ambient intelligence in PUS. In this paper, we propose an architecture for a PUS system, whose abilities include automated kidney detection in real time. Automated kidney detection is performed by training the Viola–Jones algorithm with a good set of kidney data consisting of diversified shapes and sizes. It is observed that the kidney detection algorithm delivers very good performance in terms of detection accuracy. The proposed PUS with kidney detection algorithm is implemented on a single Xilinx Kintex-7 FPGA, integrated with a Raspberry Pi ARM processor running at 900 MHz.
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机译：使用便携式超声扫描（PUS）的床边诊断可提供具有各种临床优势的舒适诊断，通常，超声扫描仪的信噪比很差，并且在现场护理设备的医生可能未受过足够的培训执行高级诊断。通过将环境情报纳入PUS，可以消除这种情况。在本文中，我们提出了PUS系统的体系结构，其功能包括实时自动肾脏检测。通过对Viola-Jones算法进行训练，并使用一组由多种形状和大小组成的良好肾脏数据来进行肾脏自动检测。可以观察到，肾脏检测算法在检测准确性方面提供了非常好的性能。带有肾脏检测算法的拟议PUS在单个Xilinx Kintex-7 FPGA上实现，并与以900 MHz运行的Raspberry Pi ARM处理器集成在一起。
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