A Computer Aided Diagnosis System for Malignant Tumors Using FDG-PET/CT Images

2007 ◽  
Vol 63 (12) ◽  
pp. 1460-1463 ◽  
Author(s):  
Hidekata Hontani
Keyword(s):  
Fdg Pet ◽  
Malignant Tumors ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Pet Ct ◽  
Computer Aided ◽  
Fdg Pet Ct ◽  
Aided Diagnosis

An effective computer aided diagnosis model for pancreas cancer on PET/CT images

2018 ◽  
Vol 165 ◽  
pp. 205-214 ◽  
Author(s):  
Siqi Li ◽  
Huiyan Jiang ◽  
Zhiguo Wang ◽  
Guoxu Zhang ◽  
Yu-dong Yao
Keyword(s):  
Pancreas Cancer ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Pet Ct ◽  
Computer Aided ◽  
Diagnosis Model ◽  
Aided Diagnosis

Computer aided diagnosis system for lung cancer based on helical CT images

1997 ◽  
Author(s):  
Shunsuke Toshioka ◽  
Keizo Kanazawa ◽  
Noboru Niki ◽  
Hitoshi Satoh ◽  
Hironobu Ohmatsu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Helical Ct ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis system for lung cancer based on retrospective helical CT images

1999 ◽  
Author(s):  
Hitoshi Satoh ◽  
Yuji Ukai ◽  
Noboru Niki ◽  
Kenji Eguchi ◽  
Kiyoshi Mori ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Helical Ct ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Development of computer-aided diagnosis system for 3D multi-detector row CT images of livers

2003 ◽  
Vol 1256 ◽  
pp. 1055-1062 ◽  
Author(s):  
Akinobu Shimizu ◽  
Takeshi Hitosugi ◽  
Jun-ya Nakagawa ◽  
Misato Tamura ◽  
Hidefumi Kobatake
Keyword(s):  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals SPNsim: A database of simulated solitary pulmonary nodule PET/CT images facilitating computer aided diagnosis

2016 ◽  
Vol 63 ◽  
pp. 357-365
Author(s):  
G. Tzanoukos ◽  
E. Athanasiadis ◽  
A. Gaitanis ◽  
A. Georgakopoulos ◽  
A. Chatziioannou ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Pet Ct ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Yanying Li ◽  
Shanshan Liu ◽  
Xianwen Yue
Keyword(s):  
Lung Cancer ◽  
Image Annotation ◽  
Lung Nodule ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Sign in / Sign up

Export Citation Format

Share Document