Vaish Pallavi, Bharath R, Rajalakshmi P
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4289-4292. doi: 10.1109/EMBC.2017.8037804.
Telesonography involves transmission of ultrasound video from remote areas to the doctors for getting diagnosis. Due to the lack of trained sonographers in remote areas, the ultrasound videos scanned by these untrained persons do not contain the proper information that is required by a physician. As compared to standard methods for video transmission, mHealth driven systems need to be developed for transmitting valid medical videos. To overcome this problem, we are proposing an organ validation algorithm to evaluate the ultrasound video based on the content present. This will guide the semi skilled person to acquire the representative data from patient. Advancement in smartphone technology allows us to perform high medical image processing on smartphone. In this paper we have developed an Application (APP) for a smartphone which can automatically detect the valid frames (which consist of clear organ visibility) in an ultrasound video and ignores the invalid frames (which consist of no-organ visibility), and produces a compressed sized video. This is done by extracting the GIST features from the Region of Interest (ROI) of the frame and then classifying the frame using SVM classifier with quadratic kernel. The developed application resulted with the accuracy of 94.93% in classifying valid and invalid images.
远程超声检查涉及将超声视频从偏远地区传输给医生以进行诊断。由于偏远地区缺乏训练有素的超声检查人员，这些未经训练的人员扫描的超声视频不包含医生所需的适当信息。与标准视频传输方法相比，需要开发由移动健康驱动的系统来传输有效的医学视频。为了克服这个问题，我们提出了一种器官验证算法，根据所呈现的内容评估超声视频。这将指导半熟练人员从患者那里获取代表性数据。智能手机技术的进步使我们能够在智能手机上进行高级医学图像处理。在本文中，我们为智能手机开发了一个应用程序（APP），它可以自动检测超声视频中的有效帧（包含清晰的器官可见性）并忽略无效帧（不包含器官可见性），并生成一个压缩大小的视频。这是通过从帧的感兴趣区域（ROI）中提取GIST特征，然后使用具有二次核的支持向量机（SVM）分类器对帧进行分类来完成的。所开发的应用程序在对有效和无效图像进行分类时的准确率为94.93%。
