Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Journal of Sun Yat-sen University(Medical Sciences)》 2016-01
Add to Favorite Get Latest Update

Preliminary Clinical Application of Interactive Image Segmentation Software in Focal Liver Lesions on Ultrasound Images

CAO Fei;LIU Guang-jian;LIN Liang;CAO Qing-xing;WANG Wei;Wang Zhu;LU Ming-de;WEN Yan-ling;Department of Ultrasound,The Sixth Affiliated Hospital,Sun Yat-Sen University;School of Advanced Computing, Sun Yat-Sen University;School of Information Science and Technology,Sun Yat-Sen University;Department of Ultrasound, The First Affiliated Hospital,Sun Yat-Sen University;  
【Objective】 To evaluate the performance of interactive segmentation software in focal liver lesions(FLL)on baseline gray scale ultrasound Images. 【Methods】 The software extracted positive and negative samples according to the foreground and background scribbles respectively and quantized candidate features. Then trained discriminative region model and discriminative boundary model respectively. Finally, combined the two histograms mentioned above in the form of a histogram to obtain the ideal result. Ideal FLL models were built and sixty ultrasound images of FLL from 45 patients were selected to test the software and compare the result with the manual segmentation by experienced radiologist. 【Results】 Taking the manual segmentation as the gold standard,the true positive ratio of segmentation software was 91.49%, the false positive ratio was 14.22%, the area overlap measure was81.05%.The average relative errors was 0.04%, and there was no statistical difference in performance between the gold standard and the software segmentation(P 0.05). 【Conclusion】 The interactive segmentation software based on discriminant model learning cansuccessfully segment FLL on baseline gray scale ultrasound images.
【Fund】: 广东省教育部产学研结合项目(2012B091000101);; 广东省产业技术研究与开发专项资金项目(2013B060500044)
【CateGory Index】: R445.1;R735.7
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved