Publication: An Automated Segmentation of R2∗ Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme
Issued Date
2018-05-01
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ISSN
15323145
03638715
03638715
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2-s2.0-85047386360
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Computer Assisted Tomography. Vol.42, No.3 (2018), 387-398
Suggested Citation
Pairash Saiviroonporn, Pornpim Korpraphong, Vip Viprakasit, Rungroj Krittayaphong An Automated Segmentation of R2∗ Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme. Journal of Computer Assisted Tomography. Vol.42, No.3 (2018), 387-398. doi:10.1097/RCT.0000000000000713 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/46697
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Title
An Automated Segmentation of R2∗ Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme
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Abstract
© 2017 Wolters Kluwer Health, Inc. All rights reserved. Objectives The objectives of this study were to develop and test an automated segmentation of R2∗ iron-overloaded liver images using fuzzy c-mean (FCM) clustering and to evaluate the observer variations. Materials and Methods Liver R2∗ images and liver iron concentration (LIC) maps of 660 thalassemia examinations were randomly separated into training (70%) and testing (30%) cohorts for development and evaluation purposes, respectively. Two-dimensional FCM used R2∗ images, and the LIC map was implemented to segment vessels from the parenchyma. Two automated FCM variables were investigated using new echo time and membership threshold selection criteria based on the FCM centroid distance and LIC levels, respectively. The new method was developed on a training cohort and compared with manual segmentation for segmentation accuracy and to a previous semiautomated method, and a semiautomated scheme was suggested to improve unsuccessful results. The automated variables found from the training cohort were assessed for their effectiveness in the testing cohort, both quantitatively and qualitatively (the latter by 2 abdominal radiologists using a grading method, with evaluations of observer variations). A segmentation error of less than 30% was considered to be a successful result in both cohorts, whereas, in the testing cohort, a good grade obtained from satisfactory automated results was considered a success. Results The centroid distance method has a segmentation accuracy comparable with the previous-best, semiautomated method. About 94% and 90% of the examinations in the training and testing cohorts were automatically segmented out successfully, respectively. The failed examinations were successfully segmented out with thresholding adjustment (3% and 8%) or by using alternative results from the previous 1-dimensional FCM method (3% and 2%) in the training and testing cohorts, respectively. There were no failed segmentation examinations in either cohort. The intraobserver and interobserver variabilities were found to be in substantial agreement. Conclusions Our new method provided a robust automated segmentation outcome with a high ease of use for routine clinical application.