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Proceedings Paper

Efficient 3D texture feature extraction from CT images for computer-aided diagnosis of pulmonary nodules
Author(s): Fangfang Han; Huafeng Wang; Bowen Song; Guopeng Zhang; Hongbing Lu; William Moore; Zhengrong Liang; Hong Zhao
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Paper Abstract

Texture feature from chest CT images for malignancy assessment of pulmonary nodules has become an un-ignored and efficient factor in Computer-Aided Diagnosis (CADx). In this paper, we focus on extracting as fewer as needed efficient texture features, which can be combined with other classical features (e.g. size, shape, growing rate, etc.) for assisting lung nodule diagnosis. Based on a typical calculation algorithm of texture features, namely Haralick features achieved from the gray-tone spatial-dependence matrices, we calculated two dimensional (2D) and three dimensional (3D) Haralick features from the CT images of 905 nodules. All of the CT images were downloaded from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), which is the largest public chest database. 3D Haralick feature model of thirteen directions contains more information from the relationships on the neighbor voxels of different slices than 2D features from only four directions. After comparing the efficiencies of 2D and 3D Haralick features applied on the diagnosis of nodules, principal component analysis (PCA) algorithm was used to extract as fewer as needed efficient texture features. To achieve an objective assessment of the texture features, the support vector machine classifier was trained and tested repeatedly for one hundred times. And the statistical results of the classification experiments were described by an average receiver operating characteristic (ROC) curve. The mean value (0.8776) of the area under the ROC curves in our experiments can show that the two extracted 3D Haralick projected features have the potential to assist the classification of benign and malignant nodules.

Paper Details

Date Published: 18 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352C (18 March 2014); doi: 10.1117/12.2043221
Show Author Affiliations
Fangfang Han, Stony Brook Univ. (China)
Northeastern Univ. (China)
Huafeng Wang, Stony Brook Univ. (United States)
Bowen Song, Stony Brook Univ. (United States)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Hongbing Lu, Fourth Military Medical Univ. (China)
William Moore, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)
Hong Zhao, Northeastern Univ. (China)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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