A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images

2018 ◽  
Author(s):  
Eduardo Romero Castro ◽  
Germán Corredor ◽  
Cheng Lu ◽  
Anant Madabhushi ◽  
Xiangxue Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Automated Detection ◽  
Feature Based

scholarly journals Automated Detection of Lung Cancer Based on Neuro Fuzzy Technique

2021 ◽  
Vol 1979 (1) ◽  
pp. 012021
Author(s):  
Y Camy Joshya ◽  
P Divyabharathi ◽  
P Archana ◽  
N Vikram
Keyword(s):  
Lung Cancer ◽  
Automated Detection ◽  
Neuro Fuzzy

scholarly journals Erratum: Automated detection of genetic abnormalities combined with cytology in sputum is a sensitive predictor of lung cancer

2008 ◽  
Vol 21 (8) ◽  
pp. 1065-1065 ◽  
Author(s):  
Ruth L Katz ◽  
Tanweer M Zaidi ◽  
Ricardo L Fernandez ◽  
Jingpin Zhang ◽  
Weigong He ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Automated Detection ◽  
Genetic Abnormalities

scholarly journals Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuhan Yang ◽  
Shoujun Zhou ◽  
Peng Shang ◽  
En Qi ◽  
Shibin Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Active Contour ◽  
Ct Images ◽  
Deformable Registration ◽  
Initial Contour ◽  
Ct Image ◽  
Speeded Up Robust Features ◽  
Contour Propagation ◽  
Feature Based ◽  
Automatic Contour

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

scholarly journals Automated detection of genetic abnormalities combined with cytology in sputum is a sensitive predictor of lung cancer

2008 ◽  
Vol 21 (8) ◽  
pp. 950-960 ◽  
Author(s):  
Ruth L Katz ◽  
Tanweer M Zaidi ◽  
Ricardo L Fernandez ◽  
Jingpin Zhang ◽  
Weigong He ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Automated Detection ◽  
Genetic Abnormalities

Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results

Lung Cancer ◽  
2018 ◽  
Vol 126 ◽  
pp. 170-173 ◽  
Author(s):  
Moritz Schwyzer ◽  
Daniela A. Ferraro ◽  
Urs J. Muehlematter ◽  
Alessandra Curioni-Fontecedro ◽  
Martin W. Huellner ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Automated Detection ◽  
Ultralow Dose ◽  
Pet Ct ◽  
Initial Results

Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening1

2004 ◽  
Vol 11 (6) ◽  
pp. 617-629 ◽  
Author(s):  
Hidetaka Arimura ◽  
Shigehiko Katsuragawa ◽  
Kenji Suzuki ◽  
Feng Li ◽  
Junji Shiraishi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Low Dose ◽  
Automated Detection ◽  
Lung Nodules ◽  
Computed Tomography Images ◽  
Low Dose Computed Tomography
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