Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature. (July 2018)

• Record Type: Journal Article
• Authors: Le, Vanbang; Yang, Dawei; Zhu, Yu; Zheng, Bingbing; Bai, Chunxue; Shi, Hongcheng; Hu, Jie; Zhai, Changwen; Lu, Shaohua
• Is Part Of: Computer methods and programs in biomedicine. Volume 160(2018)
• Page Start: 141
• Page End: 151
• Publication Date: 2018-07
• Subjects: Lung nodule classification -- K-means -- Exponential weighted -- Reference map -- Angular histogram; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A greatly robustness and highly effective pulmonary nodule classification system. The reference map is constructed using an integral image and labelled the map by K-means. Then, the grey density distribution feature is generated. Furthermore, we proposed the feature extraction method for pulmonary nodule image by designing an exponential weighted multi-angular histogram to describe each component of the grey density distribution features. The proposed feature combined with Random Forest model to classify lung nodule to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). Abstract: 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.