文献类型：期刊文献

英文题名：CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging

作者：Liu, Liping[1];Wang, Lin[1];Xu, Dan[1];Zhang, Hongjie[1,2];Sharma, Ashutosh[3];Tiwari, Shailendra[4];Kaur, Manjit[5];Khurana, Manju[6];Shah, Mohd Asif[7]

第一作者：Liu, Liping

通信作者：Zhang, HJ[1];Zhang, HJ[2];Shah, MA[3]

机构：[1]Gansu Univ Chinese Med, Affiliated Hosp, Radiol Dept, Lanzhou 73000, Gansu, Peoples R China;[2]Gansu Univ Chinese Med, Affiliated Hosp, Lanzhou 73000, Gansu, Peoples R China;[3]Southern Fed Univ, Inst Comp Technol & Informat Secur, Rostov Na Donu, Russia;[4]Apar Inst Engn & Technol, Comp Sci & Engn Dept, Patiala, Punjab, India;[5]Bennett Univ, Sch Engn & Appl Sci, Comp Sci Engn, Greater Noida 201310, India;[6]Apar Inst Engn & Technol, Comp Sci & Engn Dept, Patiala, Punjab, India;[7]Bakhtar Univ, Kabul, Afghanistan

第一机构：甘肃中医药大学第二附属医院

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Affiliated Hosp, Radiol Dept, Lanzhou 73000, Gansu, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Affiliated Hosp, Lanzhou 73000, Gansu, Peoples R China;[3]corresponding author), Bakhtar Univ, Kabul, Afghanistan.|[10735b845793de6ae2b30]甘肃中医药大学第二附属医院;[10735]甘肃中医药大学;

年份：2021

卷号：2021

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20212410493986);Scopus(收录号：2-s2.0-85107540857);WOS:【SCI-EXPANDED(收录号:WOS:000668997700003)】；

语种：英文

外文关键词：Blood - Chemotherapy - Blood vessels - Computerized tomography - Medical imaging - Tumors - Artificial intelligence - Emulsification - Image segmentation

摘要：Artificial intelligence (AI) has made various developments in the image segmentation techniques in the field of medical imaging. This article presents a liver tumor CT image segmentation method based on AI medical imaging-based technology. This study proposed an artificial intelligence-based K-means clustering (KMC) algorithm which is further compared with the region growing (RG) method. In this study, 120 patients with liver tumors in the Post Graduate Institute of Medical Education & Research Hospital, Chandigarh, India, were selected as the research objects, and they were classified according to liver function (Child-Pugh), with 58 cases in grade A and 62 cases in grade B. The experimentation indicates that liver tumor showed low density on plain CT scan, moderate enhancement in the arterial phase of the enhanced scan, and low-density filling defect in the involved blood vessel in the portal venous phase (PVP). It was observed that the CT examination is more sensitive to liver metastasis than hepatocellular carcinoma (P<0.05). The outcomes obtained depict the good deposition effect of lipiodol chemotherapy emulsion (LCTE) in the contrast group with rich blood type accounted for 53.14% and the patients with the poor blood type accounted for 25.73% showed poor deposition effect. The comparison with the state-of-the-art method reveals that the segmentation effect of the KMC algorithm is better than that of the conventional RG method.