A Probability Segmentation Algorithm for Lung Nodules Based on Three-dimensional Features_Journal of Biomedical Engineering

Authors：

SONGJia ¹ ,  NIEShengdong ¹ , WANGYuanjun ¹ , LUWen ²

1. Institue of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Department of Radiology, Taishan Medical School, Taian 271016, China;

Corresponding author：

NIEShengdong, Email: nsd4647@163.com

Keywords：

CT image; lung nodule; three-dimensional feature; probability segmentation

DOI：

10.7507/1001-5515.20140144

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Abstract Full text Figures/Tables Video References Cited by

This paper presents a probability segmentation algorithm for lung nodules based on three-dimensional features. Firstly, we computed intensity and texture features in region of interest (ROI) pixel by pixel to get their feature vector, and then classified all the pixels based on their feature vector. At last, we carried region growing on the classified result, and got the final segmentation result. Using the public Lung Imaging Database Consortium (LIDC) lung nodule datasets, we verified the performance of proposed method by comparing the probability map within LIDC datasets, which was drawn by four radiology doctors separately. The experimental results showed that the segmentation algorithm using three-dimensional intensity and texture features would be effective.

Citation： SONGJia, NIEShengdong, WANGYuanjun, LUWen. A Probability Segmentation Algorithm for Lung Nodules Based on Three-dimensional Features. Journal of Biomedical Engineering, 2014, 31(4): 771-776. doi: 10.7507/1001-5515.20140144 Copy

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1. XU N, AHUJA N, BANSAL R. Automated lung nodule segmentation using dynamic programming and EM based Classification[C]//Proceedings of SPIE--The International Society for Optical Engineering. San Diego, CA, United states: 2002, 4684: 666-676.
2. DEHMESHKI J, AMIN H, VALDIVIESO M, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach[J]. IEEE Trans Med Imaging, 2008, 27(4): 467-480.
3. 曹蕾,路利军,杨蕊梦,等.基于区域增长的肺结节自适应形态分割[J].南方医科大学学报,2008,28(12):2109-2112,2125.
4. WANG J H, ENGELMANN R, LI Q. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique[J]. Med Phys, 2007, 34(12): 4678-4689.
5. 马建国.CT图像中基于三维活动轮廓的肺结节分割[D].武汉:华中科技大学,2008.
6. ZINOVEVA O, ZINOVEV D, SIENA S A, et al. A texture-based probabilistic approach for lung nodule segmentation[C]//Image Analysis and Recognition--8th International Conference. Burnaby, BC, Canada: 2011, 6754:21-30.
7. TSAI F, CHANG C K, RAU J Y, et al. 3D computation of gray level co-occurrence in hyperspectral image cubes[C]//Proceedings of the 6th International Conference, EMMCVPR. Ezhou, China: 2007, 4679: 429-440.
8. HARALICK R M, SHANMUGAM K, DINSTEIN I H. Textural features for image classification[J]. IEEE Trans Syst Man Cyber, 1973, 3(6): 610-621.
9. ULABY F T, KOUYATE F, BRISCO B, et al. Textural information in SAR images[J]. IEEE Trans Geosci Remote Sens, 1986, 24(2): 235-245.
10. 薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析[J].电子学报,2006,34(1):155-158,134.

Journal of Biomedical Engineering

A Probability Segmentation Algorithm for Lung Nodules Based on Three-dimensional Features

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