No abstract

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.

Ultrasound imaging has been widely used for preliminary diagnosis as it is non-invasive and has good scope for the doctors to analyze many diseases. Lack of trained sonographers make ultrasound imaging diagnosis time consuming to detect any abnormality. Sometimes the problem cannot exactly be identified which may lead to error in diagnosis. Hence in this paper we present computer aided automatic detection of abnormality in kidney on the ultrasound system itself, to decrease the time for reports and not to depend on the sonographer. We classified the kidney as normal and abnormal case. Segment the kidney region and extract Intensity histogram features and Haralick features from Gray Level Cooccurnace Matrix (GLCM). These features are calculated for a set of large data containing both normal and abnormal cases. Abnormal case includes kidney stone, cyst and bacterial infection. Standard deviation for each parameter is observed, considered only those features with less deviation and implemented on FPGA Kintex board. If the range of mean value is 1.08 to 1.336, skewness is 2.882 to 7.708, Kurtosis is 1.06 to 71.152, Cluster Shade is 72 to 243, Homogeneity is 0.993 to 0.998, the observed kidney image is normal otherwise abnormal.

Ultrasound imaging uses high-frequency sound waves in medical imaging like obstetric diagnosis, stones in kidney etc. As ultrasound images are captured in real-time, they can show movement of the body's internal organs as well as blood flowing through blood vessels. In this paper medical B-mode architecture of the backend system is implemented in Kintex-7 FPGA platform. The backend processing consists of envelope detection which uses fixed filter coefficients for Hilbert transformation, log compression technique to achieve the desired dynamic range for display and image enhancement technique to increase the contrast. In-phase and quadrature phase components are computed using envelope detection block, whose absolute value is compressed to fit the dynamic range of display and interpolated to avoid artifacts while displaying. Further full scale contrast stretch enhancement technique is used to improve the image clarity. The implementation of the backend algorithms along with image enhancement technique on FPGA, show that the resolution of display is improved and also the hardware resource utilization is minimized leading to compact design for portable ultrasound systems.