Classification of Benign and Malignant Pulmonary Nodules Based on Radiomics and Random Forests Algorithm

被引：2
|
作者：
Li X. [1 ]
Li B. [1 ]
Tian L. [1 ]
Zhu W. [2 ]
Zhang L. [1 ]
机构：
[1] School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong
[2] School of Automation, Foshan University, Foshan, 528000, Guangdong
来源：
Li, Bin (binlee@scut.edu.cn) | 2018年 / South China University of Technology卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Image classification; Malignancy; Pulmonary nodules; Radiomics; Random forests; Random walker;
D O I：
10.3969/j.issn.1000-565X.2018.08.011
中图分类号：
学科分类号：
摘要：
To address the problem of low accuracy of the existing algorithms for classification of benign and malignant pulmonary nodules, a classification of benign and malignant pulmonary nodules based on radiomics and random forests algorithm is proposed in this paper. Firstly, a novel multi-scale dot enhancement filter is proposed for pulmonary nodule enhancement. Then, thresholding, shape index and texture features are used to automatically acquire the seeds. The acquired seeds are injected into the random walker algorithm, thus accurate segmentation of pulmonary nodules is achieved. Secondly, the intensity, texture, shape, wavelet, and clinical features are extracted from the segmented pulmonary nodules. Finally, random forest is employed to construct the predictive model for classifying benign and malignant pulmonary nodules. The LIDC database is used to train the predictive model. The experimental results demonstrate that the proposed algorithm has the high classification performance for classification of benign and malignant pulmonary nodules, and then accuracy, sensitivity and specificity are 94%, 92% and 94%, respectively. © 2018, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码：72 / 80
页数：8
相关论文
共 29 条
  • [1] Siegel R.L., Miller K.D., Jemal A., Cancer statistics, 2018, CA: A Cancer Journal for Clinicians, 68, 1, pp. 7-30, (2017)
  • [2] Bach P.B., Mirkin J.N., Oliver T.K., Et al., Benefits and harms of CT screening for lung cancer: a systematic review, Journal of the American Medical Association, 307, 22, pp. 2418-2429, (2012)
  • [3] Wang H., Guo X.H., Jia Z.W., Et al., Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image, European Journal of Radiology, 74, 1, pp. 124-129, (2010)
  • [4] Wu H., Sun T., Wang J., Et al., Combination of radiological and gray level co-occurrence matrix features used to distinguish solitary pulmonary nodules by computed tomography, Journal of Digital Imaging, 26, 4, pp. 797-802, (2013)
  • [5] Han F., Wang H., Zhang G., Et al., Texture feature ana-lysis for computer-aided diagnosis on pulmonary nodules, Journal of Digital Imaging, 28, 1, pp. 99-115, (2015)
  • [6] Frejlichowski D., An experimental comparison of seven shape descriptors in the general shape analysis problem, Proceedings of the Seventh International Conference on Image Analysis and Recognition, pp. 294-305, (2010)
  • [7] Ciompi F., Jacobs C., Scholten E.T., Et al., Bag-of-frequencies: a descriptor of pulmonary nodules in computed tomography images, IEEE Transactions on Medical Imaging, 34, 4, pp. 962-973, (2015)
  • [8] Farag A., Ali A., Graham J., Et al., Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest, Proceedings of 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro., pp. 169-172, (2011)
  • [9] Iwano S., Nakamura T., Kamioka Y., Et al., Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT, Computerized Medical Imaging and Graphics, 32, 5, pp. 416-422, (2008)
  • [10] Lin P.L., Huang P.W., Lee C.H., Et al., Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model, Pattern Recognition, 46, 12, pp. 3279-3287, (2013)
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