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Sensors and Materials
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Sensors and Materials,
Volume 30, Number 8(2) (2018)

Copyright(C) MYU K.K. All Rights Reserved.
pp. 1859-1868
S&M1643
https://doi.org/10.18494/SAM.2018.1899
Published: August 31, 2018

Lung Nodule Detection Using Ensemble Classifier in Computed Tomography Images [PDF]

Chien-Cheng Lee, Shuo-Ting Tsai, and Chin-Hua Yang

(Received April 5, 2017; Accepted February 27, 2018)

Keywords: lung nodule, ensemble classifier, CT image

Lung cancer is the leading cause of cancer deaths. The main reason is that patients are mostly diagnosed with lung cancer in its third or final stage. Lung nodules are small growing tissues, which may become malignant tumors that cause early lung cancer lesions. Therefore, a computer-aided system of lung nodule detection would achieve early detection and facilitate early treatment. In this paper, we present a method of lung nodule detection in computed tomography (CT) images based on an ensemble classifier. The proposed nodule detection method includes lung parenchyma segmentation, nodule candidate detection, and nodule candidate classification. First, an adaptive thresholding algorithm is applied to segment the lung parenchyma. The lung region boundaries are also corrected by using a contour analysis algorithm. Second, the adaptive thresholding algorithm is employed to find the regions of interest. Meanwhile, lung nodule candidates are roughly detected by connected component analysis. To obtain a complete 3D structure, the method merges the rough detection results if they conform to the predefined merging conditions. Finally, a self-organizing map (SOM) algorithm is used to select the negative samples for the training data, and an ensemble classifier is applied to recognize the nodule regions. The experimental results show that the proposed method outperforms the previous methods.

Corresponding author: Chien-Cheng Lee


Cite this article
Chien-Cheng Lee, Shuo-Ting Tsai, and Chin-Hua Yang, Lung Nodule Detection Using Ensemble Classifier in Computed Tomography Images, Sens. Mater., Vol. 30, No. 8, 2018, p. 1859-1868.


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