Paper Info
Title
Ultrasonography Image Analysis for Detection and Classification of Chronic Kidney Disease
Abstract
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 in painting, 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.
Year
DOI
Venue
2012
10.1109/CISIS.2012.180
CISIS
Keywords
Field
DocType
contour detection,chronic kidney disease (ckd),ultrasonography image analysis,image processing,diseases,selected representative ckd image,image inpainting,k-means clustering,noise filtering,ultrasonographic diagnosis,cardiovascular diseases,biomedical ultrasonics,early prophylaxis,cardiovascular disease,fibrosis conditions,objective computer-aid diagnosis tool,total variation filter,chronic kidney disease,kidney tissue,ckd identification,ultrasonography image,tissue clustering,kidney disease,kidney,local contrast enhancement,computer-aided diagnosis tool,kidney tissues,quantitative indicator measuring,medical image processing,reliable diagnosis,acoustics,noise,k means clustering
Ultrasonography,Computer science,Image processing,Kidney disease,Radiology,Atrophy,Medical diagnosis,Ultrasonic imaging,Kidney,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4673-1233-2
0
0.34
References 
Authors
3
7
Name
Order
Citations
PageRank
Chih-Yin Ho1111.12
Tun-Wen Pai212729.71
Yuan-Chi Peng300.34
Chien-Hung Lee401.35
Yung-Chih Chen541339.89
Yang-Ting Chen600.68
Kuo-Su Chen700.68