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Review
. 2018 Aug;31(4):451-463.
doi: 10.1007/s10278-017-0029-8.

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

José Raniery Ferreira Jr  1 , Marcelo Costa Oliveira  2 , Paulo Mazzoncini de Azevedo-Marques  3
Affiliations
Review

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

José Raniery Ferreira Jr et al. J Digit Imaging. 2018 Aug.

Abstract

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

Keywords: Image classification; Lung cancer; Pattern recognition; Pulmonary nodule.

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Conflict of interest statement

Conflict of interests

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study formal consent is not required. This study used a public image database (10.7937/K9/TCIA.2015.LO9QL9SX), which all protected health information (PHI) contained within the DICOM headers of the images were removed in accordance with Health Insurance Portability and Accountability Act (HIPAA) guidelines.

Figures

Fig. 1
Fig. 1
ROC curves for diagnostic performance of the classifiers using the unbalanced dataset
Fig. 2
Fig. 2
ROC curves for diagnostic performance of the classifiers using the balanced dataset
Fig. 3
Fig. 3
ROC curves of relevant classification scenarios with no statistically significant difference identified on sensitivity and specificity. Scenarios are presented with the number of features + classifier + dataset used
See this image and copyright information in PMC

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