An automated lung nodule detection system for CT images using synthetic minority oversampling

2016 ◽  
Author(s):  
Shrikant A. Mehre ◽  
Sudipta Mukhopadhyay ◽  
Anirvan Dutta ◽  
Nagam Chaithan Harsha ◽  
Ashis Kumar Dhara ◽  
...  
Keyword(s):  
Detection System ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection

A novel computer-aided lung nodule detection system for CT images

2011 ◽  
Vol 38 (10) ◽  
pp. 5630-5645 ◽  
Author(s):  
Maxine Tan ◽  
Rudi Deklerck ◽  
Bart Jansen ◽  
Michel Bister ◽  
Jan Cornelis
Keyword(s):  
Detection System ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided

A KNOWLEDGE-BASED LUNG NODULE DETECTION SYSTEM FOR HELICAL CT IMAGES

2006 ◽  
Vol 06 (03) ◽  
pp. 371-387
Author(s):  
HONGSHUN SU ◽  
RAVI SANKAR ◽  
WEI QIAN
Keyword(s):  
Detection System ◽  
Lung Nodule ◽  
Semantic Networks ◽  
Helical Ct ◽  
Ct Images ◽  
Lung Model ◽  
Model Composite ◽  
Lung Nodule Detection ◽  
Knowledge Based ◽  
Nodule Detection

In this paper, we describe a knowledge-based system for segmenting and labeling lung nodule on helical CT images. The system was developed under a blackboard environment that incorporates a lung knowledge model, image processing model, inference engine and a blackboard. Lung model, which contains both analogical and propositional knowledge about lung in the form of semantic networks, was used to guide the interpretation process. The system works in a hierarchical structure, from large structures to the final nodule candidates by focusing on the interested region step by step. The symbolic variables, introduced to accomplish high-level inference, were defined by fuzzy confidence functions in the lung model. Composite fuzzy functions were applied to evaluate the plausibility of the mapping between the image and lung model objects. Anatomical lung segments knowledge was embedded in the system to direct 3D validation of suspicious objects. Structures were identified and abnormal objects were reported. The experimental results obtained demonstrate the proof of concept and the potential of the automated knowledge-based lung nodule detection system.

Automatic lung nodule detection system using image processing techniques in computed tomography

2020 ◽  
Vol 56 ◽  
pp. 101659 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chang-Chiun Huang ◽  
Jing-Jhong Siao ◽  
Chia-Wen Hsieh ◽  
Vu Quang Huy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Detection System ◽  
Lung Nodule ◽  
Image Processing Techniques ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Processing Techniques

Automatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks

2019 ◽  
Vol E102.A (10) ◽  
pp. 1364-1373
Author(s):  
Furqan SHAUKAT ◽  
Kamran JAVED ◽  
Gulistan RAJA ◽  
Junaid MIR ◽  
Muhammad Laiq Ur Rahman SHAHID
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection
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