scholarly journals A Context Based Automated System for Lung Nodule Detection in CT Images

2021 ◽  
Author(s):  
Maciej Dajnowiec
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Ct Images ◽  
Automated System ◽  
Data Sets ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Testing Data ◽  
Fully Automatic ◽  
Ct Data

This thesis is focused on automatic lung nodule detection in CT images. CAD systems are suited for this tak because the sheer volume of information present in CT data sets is overwhelming for radiologists to process. The system developed in this thesis presents a fully automatic solution that applies a sequential algoriths which strongly focuses on nodule context. The system operates at a rate of 80% sensitivity with 3.05 FPs per slice. Our testing data, consisting of 19 CTdata sets containing239 lung nodules, is extremely robust when compared with other documented systems. In addition it introduces many new approaches such as a tight bounding, vessel connectivity, perimeter analysis, adaptive MLT and region growing based lung segmentation. The experimental results produced by this systemare an affirmation of the competitiveness of its performance when compared to other documented approaches.

scholarly journals A Context Based Automated System for Lung Nodule Detection in CT Images

2021 ◽  
Author(s):  
Maciej Dajnowiec
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Ct Images ◽  
Automated System ◽  
Data Sets ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Testing Data ◽  
Fully Automatic ◽  
Ct Data

This thesis is focused on automatic lung nodule detection in CT images. CAD systems are suited for this tak because the sheer volume of information present in CT data sets is overwhelming for radiologists to process. The system developed in this thesis presents a fully automatic solution that applies a sequential algoriths which strongly focuses on nodule context. The system operates at a rate of 80% sensitivity with 3.05 FPs per slice. Our testing data, consisting of 19 CTdata sets containing239 lung nodules, is extremely robust when compared with other documented systems. In addition it introduces many new approaches such as a tight bounding, vessel connectivity, perimeter analysis, adaptive MLT and region growing based lung segmentation. The experimental results produced by this systemare an affirmation of the competitiveness of its performance when compared to other documented approaches.

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

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

scholarly journals A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

2018 ◽  
Vol 232 ◽  
pp. 02001 ◽  
Author(s):  
Li Zheng ◽  
Yiran Lei
Keyword(s):  
Differential Operator ◽  
Active Contour ◽  
Lung Nodule ◽  
Region Growing ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Computed Tomography Images ◽  
Nodule Detection ◽  
Significant Step ◽  
Low Sensitivity

The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.

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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