Smartphone based automatic abnormality detection of kidney in ultrasound images

Vaish, P and Bharath, R and P, Rajalakshmi and Desai, U B (2016) Smartphone based automatic abnormality detection of kidney in ultrasound images. In: IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 14-16 Sept. 2016.

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Abstract

elesonography suffers from inherent limitations due to the need of all time availability of experts in cloud and data connectivity to the device. Computer-aided diagnosis (CAD) used for automatic detection of abnormalities without manual intervention can overcome these limitations. Commercially available ultrasound scanners restrict the installation of new softwares and hence CAD algorithms cannot be integrated into the existing ultrasound scanners. There is a need for an external computing device, which can acquire image data from ultrasound scanners, perform CAD and generate result. Smart-phones are now widely used in personalized healthcare due to its ubiquitous computing capability. Smartphones with embedded CAD can be used as a computing device for automated diagnosis. In this paper, we have developed an Application (APP) for a smartphone to automatically diagnose the kidney in the ultrasound image. With the developed APP, the smartphone can acquire images from any ultrasound scanner, process it and give the diagnostic result. Automatic abnormality detection of kidney is based on Viola Jones algorithm, texture feature extraction followed by SVM classifier. Stones and cysts are the abnormalities detected using the algorithm. The developed APP resulted with an accuracy of 90.91% in detecting the abnormalities.

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