scholarly journals Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

2002 ◽  
Vol 29 (11) ◽  
pp. 2552-2558 ◽  
Author(s):  
Metin N. Gurcan ◽  
Berkman Sahiner ◽  
Nicholas Petrick ◽  
Heang-Ping Chan ◽  
Ella A. Kazerooni ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Preliminary Evaluation ◽  
Lung Nodule Detection ◽  
Computed Tomography Images ◽  
Thoracic Computed Tomography ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

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 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

Clinical Utility of Computer‐Aided Diagnosis of Vertebral Fractures From Computed Tomography Images

2020 ◽  
Vol 35 (12) ◽  
pp. 2307-2312
Author(s):  
Nithin Kolanu ◽  
Elizabeth J Silverstone ◽  
Bao H Ho ◽  
Hiep Pham ◽  
Ash Hansen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Vertebral Fractures ◽  
Clinical Utility ◽  
Computer Aided Diagnosis ◽  
Computed Tomography Images ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis of liver tumors on computed tomography images

2017 ◽  
Vol 145 ◽  
pp. 45-51 ◽  
Author(s):  
Chin-Chen Chang ◽  
Hong-Hao Chen ◽  
Yeun-Chung Chang ◽  
Ming-Yang Yang ◽  
Chung-Ming Lo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Liver Tumors ◽  
Computer Aided Diagnosis ◽  
Computed Tomography Images ◽  
Computer Aided ◽  
Aided Diagnosis
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