Performance analysis for computer-aided lung nodule detection on LIDC data

2007 ◽  
Author(s):  
Roland Opfer ◽  
Rafael Wiemker
Keyword(s):  
Performance Analysis ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Computer-aided lung nodule detection on high-resolution CT data

2002 ◽  
Author(s):  
Rafael Wiemker ◽  
Patrick Rogalla ◽  
Andre Zwartkruis ◽  
Thomas Blaffert
Keyword(s):  
High Resolution ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
High Resolution Ct ◽  
Nodule Detection ◽  
Computer Aided ◽  
Ct Data

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection

1998 ◽  
Vol 17 (6) ◽  
pp. 872-880 ◽  
Author(s):  
M.G. Penedo ◽  
M.J. Carreira ◽  
A. Mosquera ◽  
D. Cabello
Keyword(s):  
Neural Network ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

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
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