Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation
Joint Authors
Li, Siqi
Li, Shaojie
Jiang, Huiyan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment.
However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast.
This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images.
First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently.
Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation.
Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information.
Finally, tumor segmentation is performed using another ELM.
Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works.
American Psychological Association (APA)
Jiang, Huiyan& Li, Shaojie& Li, Siqi. 2018. Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation. BioMed Research International،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1129223
Modern Language Association (MLA)
Jiang, Huiyan…[et al.]. Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation. BioMed Research International No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1129223
American Medical Association (AMA)
Jiang, Huiyan& Li, Shaojie& Li, Siqi. Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1129223
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129223