Development of detection system of kidney and non-kidney images for different ultrasound machines - Shaharuddin, Nurul Aimi (2020)
The ultrasound machine is a well-known medical equipment being used widely to measure kidney size, shape, and position as well as assist the user in diagnosing kidney abnormalities such as stone, infection, and cysts. The interpretation of an ultrasound image totally depends on the operator’s knowledge, skill and experience which may be prone to misdiagnose. Previous researchers have built several detection systems to detect kidney ultrasound images but does not consider the practical use of the detection system in different ultrasound machines. This research proposes a detection system which is able to detect the kidney ultrasound images regardless of the source of ultrasound images. The detection system reduced the noise appeared in ultrasound kidney image, enhanced the image and detect the kidney image regardless of the source of the ultrasound kidney images. 188 kidney and non-kidney images were used in the developing the system. The system was developed using MATLAB where the images were enhanced using histogram equalization, filtered by Wiener filter, and segmented manually by users using the mask tool. Then, texture analysis was done using correlation in Gray Level Co-occurrence Matrix (GLCM) and classification was performed using decision tree. Next, the detection system was developed by using GUIDE MATLAB. In evaluating the effectiveness of the system, performance analysis using confusion matrix was carried out. Performance analysis showed that the system can detect kidney and non-kidney images from 4 different types of US machines with a percentage accuracy of 72.97% while the sensitivity of the system is 81.97%. The system specificity is 30.77%. The development of this detection system for ultrasound kidney images has shown a promise and could be further improved for better performances. This detection system hopefully could help the operator in getting a second opinion on interpretation of ultrasound kidney images thus minimizing the human error in misinterpretation of the images.