Ultrasonography image analysis for detection and classification of chronic kidney disease

More than 5% of adults suffer from different types of kidney disease, and millions of people die prematurely from cardiovascular diseases associated with chronic kidney disease (CKD) in each year. The best way to reduce death caused by kidney disease is early prophylaxis and treatment, and which could be achieved through accurate and reliable diagnoses at the early stage. Among various diagnostic methods, ultrasonographic diagnosis is a low-cost, convenient, non-invasive, and timeliness method. Most importantly, this type inspection would not cause extra burden for patients who suffer kidney diseases. This paper presents a computer-aided diagnosis tool based on analyzing ultrasonography images, and the developed system could detect and classify different stages of CKD. The image processing techniques focus on detecting the atrophy of kidney and the proportion of fibrosis conditions within kidney tissues. The system includes image inpainting, noise filtering, contour detection, local contrast enhancement, tissue clustering, and quantitative indicator measuring for distinguishing various stages of CKD. This study has collected thousands of ultrasonic images from patients with kidney diseases, and the selected representative CKD images were applied to be pre-analyzed and trained for comparison. The calculated transition locations as reference indicators could provide physicians an auxiliary and objective computer-aid diagnosis tool for CKD identification and classification.