A computer aided diagnosis system for detection of lung nodules from series of CT slices

2017 ◽  
Author(s):  
May Phu Paing ◽  
Somsak Choomchuay
Keyword(s):  
Computer Aided Diagnosis ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Ct Slices

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.

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM

2016 ◽  
Vol 55 (8) ◽  
pp. 1129-1146 ◽  
Author(s):  
Antonio Oseas de Carvalho Filho ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Rodolfo Acatauassú Nunes ◽  
Marcelo Gattass
Keyword(s):  
Computed Tomography ◽  
Genetic Algorithm ◽  
Shape Analysis ◽  
Computer Aided Diagnosis ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

2004 ◽  
Vol 182 (2) ◽  
pp. 505-510 ◽  
Author(s):  
Shingo Kakeda ◽  
Junji Moriya ◽  
Hiromi Sato ◽  
Takatoshi Aoki ◽  
Hideyuki Watanabe ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis
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