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http://dx.doi.org/10.3837/tiis.2019.01.020

An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features  

Hao, Rui (College of Information Management, Shanxi University of Finance & Economics)
Qiang, Yan (College of Computer Science and Technology, Taiyuan University of Technology)
Liao, Xiaolei (College of Computer Science and Technology, Taiyuan University of Technology)
Yan, Xiaofei (Data center, Bank of China)
Ji, Guohua (Department of Computer Science and Technology, Xinzhou Teachers University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.1, 2019 , pp. 347-370 More about this Journal
Abstract
In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.
Keywords
pulmonary nodule detection; multi-scale enhancement filter; feature descriptor; SVM;
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