Abstract
The study aims to improve the performance of current computer-aided schemes for the detection of lung lesions, especially the low-contrast in gray density or irregular in shape. The relative position between suspected lesion and whole lung is, for the first time, added as a latent feature to enrich current Three-dimensional (3D) features such as shape, texture. Subsequently, 3D matrix patterns-based Support Vector Machine (SVM) with the latent variable, referred to as L-SVM3Dmatrix, was constructed accordingly. A CT image database containing 750 abnormal cases with 1050 lesions was used to train and evaluate several similar computer-aided detection (CAD) schemes: traditional features-based SVM (SVMfeature), 3D matrix patterns-based SVM (SVM3Dmatrix) and L-SVM3Dmatrix. The classifier performances were evaluated by computing the area under the ROC curve (AUC), using a 5-fold cross-validation. The L-SVM3Dmatrix sensitivity was 93.0 with 1.23 % percentage of False Positive (FP), the SVM3Dmatrix sensitivity was 88.4 with 1.49 % percentage of FP, and the SVMfeature sensitivity was 87.2 with 1.78 % percentage of FP. The L-SVM3Dmatrix outperformed other current lung CAD schemes, especially regarding the difficult lesions.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61301257), Science and Technology Development Plan of the Jilin Province (201201107) and the Doctoral Scientific Research Fund of Northeast Dianli University (bsjxm-201104).
Conflict of interest
Qingzhu Wang, Wenchao Zhu, and Bin Wang declare that they have no conflict of interest.
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This article is part of the Topical Collection on Systems-Level Quality Improvement
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Wang, Q., Zhu, W. & Wang, B. Three-Dimensional SVM with Latent Variable: Application for Detection of Lung Lesions in CT Images. J Med Syst 39, 171 (2015). https://doi.org/10.1007/s10916-014-0171-5
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DOI: https://doi.org/10.1007/s10916-014-0171-5