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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44268

Title: Ultrasonography Image Analysis for Detection and Classification of Chronic Kidney Disease
Authors: Chih-Yin Ho
Tun-Wen Pai
Yuan-Chi Peng
Chien-Hung Lee
Yung-Chih Chen
Yang-Ting Chen
Kuo-Su Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: ultrasonography image
chronic kidney disease (CKD)
image inpainting
K-means clustering
total variation filter
Date: 2012-07
Issue Date: 2017-11-21T06:21:52Z
Publisher: The 6th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2012)
Abstract: 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.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44268
Appears in Collections:[資訊工程學系] 演講及研討會

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