CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging

Liping Liu,Shailendra Tiwari,Lin Wang,Manju Khurana,Mohd Asif Shah,Hongjie Zhang,Dan Xu,Ashutosh Sharma,Manjit Kaur,Ahmed A Abd El-Latif

doi:10.1155/2021/9919507

Abstract

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.

Highlights

Liping Liu,1 Lin Wang,1 Dan Xu,1 Hongjie Zhang,1,2 Ashutosh Sharma,3 Shailendra Tiwari,4 Manjit Kaur,5 Manju Khurana,6 and Mohd Asif Shah 7
It was observed that the computed tomography (CT) examination is more sensitive to liver metastasis than hepatocellular carcinoma (P < 0.05). e 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. e comparison with the state-of-the-art method reveals that the segmentation effect of the K-means clustering (KMC) algorithm is better than that of the conventional region growing (RG) method
All 120 patients were examined by CT images. e diagnostic results are analyzed in terms of sensitivity and specificity which are obtained by using the values of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN)

Summary

Introduction

Liping Liu ,1 Lin Wang ,1 Dan Xu ,1 Hongjie Zhang ,1,2 Ashutosh Sharma ,3 Shailendra Tiwari ,4 Manjit Kaur ,5 Manju Khurana ,6 and Mohd Asif Shah 7. Artificial intelligence (AI) has made various developments in the image segmentation techniques in the field of medical imaging. Is article presents a liver tumor CT image segmentation method based on AI medical imaging-based technology. More than 75% of patients undergoing liver tumor tissue resection have suffered from the relapse. Erefore, in clinical medicine, there is an urgent need for a diagnosis method to effectively identify the liver tumor lesion area. With the continuous advancement of medical imaging technology, CT takes computer equipment as the core and greatly improves the clinical diagnostic efficacy of various diseases. Due to the rapid inspection of CT images, clear images, and low-cost difference, it is the most common detection method in the diagnosis of liver tumors [14,15,16]

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Discussion

Conclusion