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Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

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Abstract

Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90 % differentiation rate, the Haralick features achieved the highest AUC value of 92.70 % at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.

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Acknowledgments

This work was partly supported by the NIH/NCI under grant nos. CA143111 and CA082402, and a PSC-CUNY award 65230-0043. This work was also supported by the National Science Foundation of China under grant nos. 61071213, 61172002, 81071220, and 81230035, the National Key Technologies R&D Program of China under grant no. 2011BAI12B03, the Fundamental Research Funds for the Central Universities under grant no. N120518001, and Liaoning Natural Science Foundation 2013020021.

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Authors and Affiliations

  1. Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA

    Fangfang Han, Huafeng Wang, Hao Han, Bowen Song, William Moore & Zhengrong Liang

  2. Northeastern University, Shenyang, Liaoning, 110819, China

    Fangfang Han & Hong Zhao

  3. Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi, 710032, China

    Guopeng Zhang & Hongbing Lu

  4. Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, NY, 11794, USA

    Bowen Song

  5. Department of Engineering Science and Physics, City University of New York/CSI, Staten Island, NY, 10314, USA

    Lihong Li

  6. State University of New York, 4th Floor, Room 120/HSC, Stony Brook, NY, 11794-8460, USA

    Zhengrong Liang

  7. Neusoft Company, Room 308, Shenyang, Liaoning, 110179, China

    Hong Zhao

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  1. Fangfang Han
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  2. Huafeng Wang
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  3. Guopeng Zhang
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  4. Hao Han
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  6. Lihong Li
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  7. William Moore
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  8. Hongbing Lu
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  9. Hong Zhao
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  10. Zhengrong Liang
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Corresponding authors

Correspondence to Hong Zhao or Zhengrong Liang.

Appendix

Appendix

Table 10 Features and their performance levels for nodule differentiation
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Han, F., Wang, H., Zhang, G. et al. Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules. J Digit Imaging 28, 99–115 (2015). https://doi.org/10.1007/s10278-014-9718-8

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