scholarly journals End-to-End Lung Nodule Detection in Computed Tomography

2018 ◽  
pp. 37-45 ◽  
Author(s):  
Dufan Wu ◽  
Kyungsang Kim ◽  
Bin Dong ◽  
Georges El Fakhri ◽  
Quanzheng Li
Keyword(s):  
Computed Tomography ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
End To End

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

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.

Lung nodule detection in low-dose and thin-slice computed tomography

2008 ◽  
Vol 38 (4) ◽  
pp. 525-534 ◽  
Author(s):  
A. Retico ◽  
P. Delogu ◽  
M.E. Fantacci ◽  
I. Gori ◽  
A. Preite Martinez
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Lung Nodule ◽  
Thin Slice ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images

2021 ◽  
Vol 9 (9) ◽  
pp. 1778-1790
Author(s):  
Henil Satra
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Scan ◽  
Lung Nodule ◽  
Morphological Operations ◽  
Lung Nodule Detection ◽  
Contour Segmentation ◽  
Nodule Detection ◽  
Segmentation Approach ◽  
Computed Tomography Image

Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.

Comparison of digital tomosynthesis and computed tomography for lung nodule detection in SOS screening program

2017 ◽  
Vol 122 (8) ◽  
pp. 568-574 ◽  
Author(s):  
Maurizio Grosso ◽  
◽  
Roberto Priotto ◽  
Donatella Ghirardo ◽  
Alberto Talenti ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Screening Program ◽  
Lung Nodule ◽  
Digital Tomosynthesis ◽  
Lung Nodule Detection ◽  
Nodule Detection
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