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access icon free Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

© The Institution of Engineering and Technology
Received 08/12/2016, Accepted 10/03/2018, Revised 11/11/2017, Published 13/03/2018

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.

Inspec keywords: feature extraction; Gabor filters; lung; cancer; image texture; image classification; computerised tomography; image segmentation; medical image processing

Other keywords: RF classifier; malignant classification; benign pulmonary nodules; General Hospital of Guangzhou Military Command; RFs algorithm; thoracic computed tomography images; classification method; feature vector; lung images dataset consortium dataset; random walk algorithm; pulmonary nodule segmentation; improved random forest algorithm; bootstrap method; rotation invariant uniform local binary pattern; malignant pulmonary nodules; feature subset; lung cancer; geometric texture features; malignant nodules; Gabor filter methods

Subjects: X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Knowledge engineering techniques; Image recognition; Filtering methods in signal processing; Computer vision and image processing techniques; X-rays and particle beams (medical uses); Patient diagnostic methods and instrumentation; Biology and medical computing

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