A detection approach for solitary pulmonary nodules based on CT images

It has been indicated that detection of pulmonary nodules plays an important role in diagnosing lung cancer in early-stage. In this paper, we propose an algorithm for detecting solitary pulmonary nodules automatically. Firstly, the algorithm implements prepared processing on original CT images and a...

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Veröffentlicht in:2012 2nd International Conference on Computer Science and Network Technology S. 1253 - 1257
Hauptverfasser: Shao, Hong, Cao, Li, Liu, Yang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2012
Schlagworte:
Accuracy
Classification algorithms
Computed tomography
Feature extraction
Image segmentation
Lung cancer
Lungs
solitary pulmonary nodules
specificity
SVM classifier
Training
Tumors
Wiener filters
ISBN:1467329630, 9781467329637
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Abstract It has been indicated that detection of pulmonary nodules plays an important role in diagnosing lung cancer in early-stage. In this paper, we propose an algorithm for detecting solitary pulmonary nodules automatically. Firstly, the algorithm implements prepared processing on original CT images and adopts adaptive iteration threshold twice to complete pulmonary parenchyma segmentation. Secondly, the experiment combines histogram analysis with compactness feature to obtain candidate nodules, and then achieves feature extraction for ROIs. Finally, SVM classifier is constructed on the basis of the extracted features to recognize true nodules and label them on original images. Experimental results indicate that our algorithm can not only achieve high accuracy and specificity but also can reduce the misdiagnosis, which is able to supply reference information with the radiologist detecting pulmonary nodules.
AbstractList It has been indicated that detection of pulmonary nodules plays an important role in diagnosing lung cancer in early-stage. In this paper, we propose an algorithm for detecting solitary pulmonary nodules automatically. Firstly, the algorithm implements prepared processing on original CT images and adopts adaptive iteration threshold twice to complete pulmonary parenchyma segmentation. Secondly, the experiment combines histogram analysis with compactness feature to obtain candidate nodules, and then achieves feature extraction for ROIs. Finally, SVM classifier is constructed on the basis of the extracted features to recognize true nodules and label them on original images. Experimental results indicate that our algorithm can not only achieve high accuracy and specificity but also can reduce the misdiagnosis, which is able to supply reference information with the radiologist detecting pulmonary nodules.
Author Shao, Hong
Cao, Li
Liu, Yang
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  organization: School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China
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  surname: Liu
  fullname: Liu, Yang
  organization: School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China
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Snippet It has been indicated that detection of pulmonary nodules plays an important role in diagnosing lung cancer in early-stage. In this paper, we propose an...
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StartPage 1253
SubjectTerms Accuracy
Classification algorithms
Computed tomography
Feature extraction
Image segmentation
Lung cancer
Lungs
solitary pulmonary nodules
specificity
SVM classifier
Training
Tumors
Wiener filters
Title A detection approach for solitary pulmonary nodules based on CT images
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