Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
296-299

Pulmonary nodule image grey density distribution feature extraction algorithm and adenocarcinoma benign/malignant classification

Vanbang L E1
Zhu Yu1
Zheng Bingbing1
Yang Dawei2
Ren Xiaodong1
Thiminhchinh Ngo3
1. School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
2. Zhongshan Hospital, Fudan University, Shanghai 200032, China
3. Hitech Telecommunication Center, Hanoi 10000, Vietnam
DOI: 10.19734/j.issn.1001-3695.2018.05.0505

Abstract

Aimed at lung nodule benign/malignant classification, this paper proposed an effective grey scale density distribution feature extraction algorithm which combined with pattern recognition models to evaluate the classification system. The proposed feature extraction algorithm firstly collected a large number of blocks from lung tumor images and determined the distance matrix by calculating the relationships among the image blocks. Then, it used K-means clustering methods to classify the current image blocks and obtained 10 cluster centers. After that, it calculated the distribution density features by mapping CT value of nodule image pixels with the 10 cluster centers and extracted a 10-dimensional feature vector. Finally, the algorithm divided the extracted feature vectors into training and testing set to identify lung adenocarcinomas risk levels by random forest classification model. This paper evaluated the classification framework in LIDC-IDRI dataset, and the average accuracy reached to 0.9008. The proposed method outperforms the most recent techniques, and the experimental results show great robustness of the proposed method for different lung CT image datasets.

Foundation Support

国家自然基金青年基金资助项目(81500078)
复旦大学附属中山医院临床研究专项基金资助项目(2016ZSLC05,2016ZSCX02)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0505
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Technology of Graphic & Image
Pages: 296-299
Serial Number: 1001-3695(2020)01-063-0296-04

Publish History

[2020-01-05] Printed Article

Cite This Article

Vanbang L E, 朱煜, 郑兵兵, 等. 图像灰度密度分布计算模型及肺结节良恶性分类 [J]. 计算机应用研究, 2020, 37 (1): 296-299. (Vanbang L E, Zhu Yu, Zheng Bingbing, et al. Pulmonary nodule image grey density distribution feature extraction algorithm and adenocarcinoma benign/malignant classification [J]. Application Research of Computers, 2020, 37 (1): 296-299. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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