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Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm
- Source :
- IET Image Processing. 12:1253-1264
- Publication Year :
- 2018
- Publisher :
- Institution of Engineering and Technology (IET), 2018.
-
Abstract
- Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey-level co-occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver-operating-characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance.
- Subjects :
- Contextual image classification
Local binary patterns
Computer science
Feature vector
Feature extraction
02 engineering and technology
Image segmentation
Mutual information
030218 nuclear medicine & medical imaging
Random forest
03 medical and health sciences
0302 clinical medicine
Gabor filter
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Algorithm
Software
Subjects
Details
- ISSN :
- 17519667 and 17519659
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- IET Image Processing
- Accession number :
- edsair.doi...........11a6d598bda82109d4185f75c24a7543