3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts
Joint Authors
Zhou, Zhuhuang
Wu, Weiwei
Wu, Shuicai
Zhang, Yanhua
Zhang, Rui
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer.
Despite many years of research, 3D liver tumor segmentation remains a challenging task.
In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts.
With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost.
Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy.
The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors.
We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor.
The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.
American Psychological Association (APA)
Wu, Weiwei& Wu, Shuicai& Zhou, Zhuhuang& Zhang, Rui& Zhang, Yanhua. 2017. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts. BioMed Research International،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1137492
Modern Language Association (MLA)
Wu, Weiwei…[et al.]. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts. BioMed Research International No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1137492
American Medical Association (AMA)
Wu, Weiwei& Wu, Shuicai& Zhou, Zhuhuang& Zhang, Rui& Zhang, Yanhua. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1137492
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137492