title: Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection.
abstract: Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm's robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail.
authors.affiliation: Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Lianfen
authors.identifier: 0000-0003-1620-1504
authors.initials: L
authors.lastname: Huang
authors.affiliation: Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Minghui
authors.identifier: 0000-0002-9062-8410
authors.initials: M
authors.lastname: Weng
authors.affiliation: Department of Radiology, The 476th Clinic Section, Fuzhou General Hospital of the PLA, Fuzhou, Fujian 350002, China.
authors.forename: Haitao
authors.identifier: 0000-0001-9164-9742
authors.initials: H
authors.lastname: Shuai
authors.affiliation: Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Yue
authors.identifier: 0000-0002-8544-4510
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Department of Radiology, The 476th Clinic Section, Fuzhou General Hospital of the PLA, Fuzhou, Fujian 350002, China.
authors.forename: Jianjun
authors.identifier: 0000-0002-7978-2951
authors.initials: J
authors.lastname: Sun
authors.affiliation: Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Fenglian
authors.identifier: 0000-0001-8145-4319
authors.initials: F
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*