Literature DB >> 19237341

An automatic computer-aided detection system for meniscal tears on magnetic resonance images.

Bharath Ramakrishna1, Weimin Liu, Ganesh Saiprasad, Nabile Safdar, Chein-I Chang, Khan Siddiqui, W Kim, Eliot Siegel, Jyh-Wen Chai, Clayton Chi-Chang Chen, San-Kan Lee.   

Abstract

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee.

Mesh:

Year:  2009        PMID: 19237341     DOI: 10.1109/TMI.2009.2014864

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  2 in total

1.  A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.

Authors:  M H Fazel Zarandi; A Khadangi; F Karimi; I B Turksen
Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

2.  Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image.

Authors:  YiNan Zhang; MingQiang An
Journal:  J Healthc Eng       Date:  2017-07-06       Impact factor: 2.682

  2 in total

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