期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 160, 期 -, 页码 141-151出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2018.04.001
关键词
Lung nodule classification; K-means; Exponential weighted; Reference map; Angular histogram
类别
资金
- Zhongshan Hospital Clinical Research Foundation [2016ZSLC05, 2016ZSCX02]
- National Key Scientific and Technology Support Program [2013BAI09B09]
- National Natural Science Foundation of China [81500078]
- Natural Science Foundation of Shanghai [15ZR1408700]
Background and objectives: To improve lung nodule classification efficiency, we propose a lung nodule CT image characterization method. We propose a multi-directional feature extraction method to effectively represent nodules of different risk levels. The proposed feature combined with pattern recognition model to classify lung adenocarcinomas risk to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). Methods: First, we constructed the reference map using an integral image and labelled this map using a K-means approach. The density distribution map of the lung nodule image was generated after scanning all pixels in the nodule image. An exponential function was designed to weight the angular histogram for each component of the distribution map, and the features of the image were described. Then, quantitative measurement was performed using a Random Forest classifier. The evaluation data were obtained from the LIDC-IDRI database and the CT database which provided by Shanghai Zhongshan hospital (ZSDB). In the LIDC-IDRI, the nodules are categorized into three configurations with five ranks of malignancy (1 to 5). In the ZSDB, the nodule categories are AAH, AIS, MIA, and IA. Results: The average of Student's t-test p-values were less than 0.02. The AUCs for the LIDC-IDRI database were 0.9568, 0.9320, and 0.8288 for Configurations 1, 2, and 3, respectively. The AUCs for the ZSDB were 0.9771, 0.9917, 0.9590, and 0.9971 for AAH, AIS, MIA and IA, respectively. Conclusion: The experimental results demonstrate that the proposed method outperforms the state-of-the-art and is robust for different lung CT image datasets. (c) 2018 Elsevier B.V. All rights reserved.
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