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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liping Liu ◽  
Lin Wang ◽  
Dan Xu ◽  
Hongjie Zhang ◽  
Ashutosh Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Liver Tumor ◽  
Blood Type ◽  
Arterial Phase ◽  
Low Density ◽  
Ct Image ◽  
Segmentation Method ◽  
Deposition Effect

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.

A Unified Segmentation Method for CT Image Segmentation with Contrast Agent

2006 ◽  
Author(s):  
H.-F. Lee ◽  
P.-C. Huang ◽  
C. Wietholt ◽  
C.-H. Hsu ◽  
K. M. Lin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Contrast Agent ◽  
Ct Image ◽  
Segmentation Method

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

2019 ◽  
Vol 46 (11) ◽  
pp. 4970-4982 ◽  
Author(s):  
Azael M. Sousa ◽  
Samuel B. Martins ◽  
Alexandre X. Falcão ◽  
Fabiano Reis ◽  
Ericson Bagatin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
Segmentation Method

scholarly journals Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Feng Zhu ◽  
Bo Zhang
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Image Segmentation ◽  
Pulmonary Tuberculosis ◽  
Clinical Characteristics ◽  
Ct Image ◽  
Segmentation Method ◽  
Tuberculosis Patients ◽  
Cancer Group ◽  
Image Segmentation Algorithm

Objective: We used U-shaped convolutional neural network (U_Net) multi-constraint image segmentation method to compare the diagnosis and imaging characteristics of tuberculosis and tuberculosis with lung cancer patients with Computed Tomography (CT). Methods: We selected 160 patients with tuberculosis from the severity scoring (SVR) task is provided by Image CLEF Tuberculosis 2019. According to the type of diagnosed disease, they were divided into tuberculosis combined with lung cancer group and others group, all patients were given chest CT scan, and the clinical manifestations, CT characteristics, and initial suspected diagnosis and missed diagnosis of different tumor diameters were observed and compared between the two groups. Results: There were more patients with hemoptysis and hoarseness in pulmonary tuberculosis combined with lung cancer group than in the pulmonary others group (P<0.05), and the other symptoms were not significantly different (P>0.05). Tuberculosis combined with lung cancer group had fewer signs of calcification, streak shadow, speckle shadow, and cavitation than others group; however, tuberculosis combined with lung cancer group had more patients with mass shadow, lobular sign, spines sign, burr sign and vacuole sign than others group. Conclusion: The symptoms of hemoptysis and hoarseness in pulmonary tuberculosis patients need to consider whether the disease has progressed and the possibility of lung cancer lesions. CT imaging of pulmonary tuberculosis patients with lung cancer usually shows mass shadows, lobular signs, spines signs, burr signs, and vacuoles signs. It can be used as the basis for its diagnosis. Simultaneously, the U-Net-based segmentation method can effectively segment the lung parenchymal region, and the algorithm is better than traditional algorithms. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 How to cite this:Zhu F, Zhang B. Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1705-1709. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

X-Ray CT Image Segmentation of Asphalt Concrete Based on Fuzzy C-Means

2012 ◽  
Vol 170-173 ◽  
pp. 3444-3448 ◽  
Author(s):  
Jian Jun Wei ◽  
Hai Bin Li ◽  
Cheng Wan
Keyword(s):  
Image Segmentation ◽  
Asphalt Concrete ◽  
Low Density ◽  
Ct Image ◽  
Threshold Values ◽  
Double Peak ◽  
X Ray ◽  
Fuzzy C Means ◽  
Double Peaks ◽  
Image Technique

This study focuses on the threshold segmentation algorithm to obtain the real microstructure of asphalt concrete based on digital image technique, the perlite powder which was a kind of low-density material was put in the asphalt concrete to enhance the density contrast, three different specimens in which added different contents of perlite powder were compacted, and then the asphalt concrete specimens were scanned using x-ray CT to capture the gray images that reflect the density differences of the three constituents such as aggregates, mastic and voids, the CT images were converted to be the histograms. Furthermore, the FCM (Fuzzy C-Means) was demonstrated that it could be utilized to choose proper threshold values and segment images exactly, according to the double peak conditions of the three different histograms, the double peak condition for AC-13 is the best among the three types, a similar double peak features between AK13 and SMA-13 were observed. The results shows that the different contents of perlite powder added in the asphalt concrete can form different double peaks. This is another new method to segment the three constituents of the asphalt concrete exactly.

scholarly journals Application of Artificial Intelligence in Medical Imaging Diagnosis

2020 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Linyi Zhang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Accurate Diagnosis ◽  
Data Sets ◽  
Special Role ◽  
Imaging Diagnosis ◽  
Learning Data

Both the treatment of cancer and other serious diseases often depends on the diagnosis of artificial complexity and heavy experience. The introduction of artificial intelligence in medical imaging has injected vitality into the diagnosis of images. Artificial intelligence uses deep learning, image segmentation, neural networks and other algorithms flexibly in image recognition through learning data sets to extract features for accurate diagnosis of clinical diseases. At the same time, it also plays a special role in controlling the spread of infectious diseases such as new coronary pneumonia.

CT Image Segmentation Method Combining Wavelet Transform and RSF Model

2020 ◽  
Vol 40 (21) ◽  
pp. 2110003
Author(s):  
王珏 Wang Jue ◽  
张秀英 Zhang Xiuying ◽  
蔡玉芳 Cai Yufang ◽  
卢艳平 Lu Yanping
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Ct Image ◽  
Segmentation Method

scholarly journals Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

2020 ◽  
Vol 10 ◽  
Author(s):  
Naoshi Nishida ◽  
Masatoshi Kudo
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Liver Tumor ◽  
Human Error ◽  
Liver Tumors ◽  
Data Sets ◽  
Task Setting ◽  
Expected Performance ◽  
Malignant Lesions

Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.

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