조명전기설비학회논문지 (Journal of the Korean Institute of Illuminating and Electrical Installation Engineers)

한국조명전기설비학회 (The Korean Institute of IIIuminating and Electrical Installation Engineers)
권호 표지
DOI QR코드

DOI QR Code

An Improved Texture Feature Extraction Method for Recognizing Emphysema in CT Images

  • 투고 : 2010.11.04
  • 심사 : 2010.11.19
  • 발행 : 2010.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

키워드

참고문헌

  1. M. Park, B. Kang, S.J. Jin, and S. Luo, Computer Aided Diagnosis System of Medical Images using Incremental Learning Method, Expert Systems with Applications, volume 36, pages 7242-7251, 2009. https://doi.org/10.1016/j.eswa.2008.09.058
  2. A. M. R. Schilham, B. V. Ginneken, and M. Loog, A Computer-Aided Diagnosis System for Detection of Lung Nodules in Chest Radiographs with an Evaluation on a Public Database, Medical Image Analysis, volume 10, pages 247-258, 2006. https://doi.org/10.1016/j.media.2005.09.003
  3. K. Takei, N. Homma, T. Ishibashi, M. Sakai, and M. Yoshizawa, Computer Aided Diagnosis for Pulmonary Nodules by Shape Feature Extraction, In: SICE Annual Conference 2007, Kagawa University, Takamatsu City, Japan, September 17-20, pages 1964-1967, 2007. https://doi.org/10.1109/SICE.2007.4421308
  4. H. Zhang, J. E. Fritts, and S. A. Goldman, A Fast Texture Feature Extraction Method for Region-based Image Segmentation, Image and video communications and processing, volume 5685 (2), pages 957-968, 2005. https://doi.org/10.1117/12.587899
  5. T. K. Liang, T. Tanaka, H. Nakamura and A. Ishizaka, A Neural Network based Computer-Aided Diagnosis of Emphysema using CT Lung Images, In: SICE Annual Conference 2007, Kagawa University, Takamatsu City, Japan, September 17-20, pages 703-709, 2007. https://doi.org/10.1109/SICE.2007.4421073
  6. Shao-Hu Peng, Deok-Hwan Kim, Seok-Lyong Lee, Myung-Kwan Lim, Texture Feature Extraction Based on a Uniformity Estimation Method for Local Brightness and Structure in Chest CT Images, Computers in Biology and Medicine, in press, 2010. https://doi.org/10.1016/j.compbiomed.2010.10.005
  7. Shao-Hu Peng, Khairul Muzzammil, and Deok-Hwan Kim, Quantitative Image Analysis of Chest CT Using Gray Level Local Binary Pattern Texture Feature, ICCC 2009, 2009-12-18 pp.129-132.
  8. A. H. Mir, M. Hanmandlu and S.N. Tandon, Texture Analysis of CT Images, In: Engineering in Medicine and Biology Magazine, IEEE, volume 14, pages781-786, 1995. https://doi.org/10.1109/51.473275
  9. T. Ojala, M. Pietikainen, and D. Harwood, A Comparative Study of Texture Measures with Classification Based on Feature Distributions, Pattern recognition, volume 29, pages 51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
  10. T. Ojala, M. Pietikainen, and T. Maenpaa, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume. 24, No. 7,pages 971–987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  11. Marko Heikkila, Matti Pietikainena, Cordelia Schmid, Description of interest regions with local binary patterns, Pattern Recognition, Vol.42, pp.425-436, 2009. https://doi.org/10.1016/j.patcog.2008.08.014
  12. T. Glatard, J. Montagnat, and I. E. Magnin, Texture Based Medical Image Indexing and Retrieval: Application to Cardiac Imaging, In: the 6th ACM SIGMM international workshop on Multimedia information retrieval, New York, USA, October 15-16, pages 135-142, 2004. https://doi.org/10.1145/1026711.1026734
  13. I. Valavanis, S. G. Mougiakakou, K. S. Nikita, and A. Nikita, Computer Aided Diagnosis of CT Focal Liver Lesions by an Ensemble of Neural Network and Statistical Classifiers, In: 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary, July 25-29,volume 3, pages 1929- 1934, 2004. https://doi.org/10.1109/IJCNN.2004.1380907
  14. W. L. Zhang and X. Z. Wang, Feature Extraction and Classification for Human Brain CT Image, In: the Sixth International Conference on Machine Learning andCybernetics, Hong Kong, August 19-22, volume 2, pages 1155-1159, 2007. https://doi.org/10.1109/ICMLC.2007.4370318
  15. R. M. Haralick, K. Shanmugam and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on System, Man, and Cybernetics, volume SMC-3, pages 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314
  16. R. W. Conners, and C.A.Harlow, A Theoretical Comparison of Texture Algorithms, IEEE Transactions: Pattern Analysis and Machine Intelligence, pp. 204-222, 1980. https://doi.org/10.1109/TPAMI.1980.4767008
  17. N. Otsu, A Threshold Selection Method from Gray-Level Histogram, IEEE Transactions on System Man Cybernetics, volume SMC-9, No.1, pages 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  18. B. Park, Y. R. Chen, Co-occurrence Matrix Texture Features of Multi-spectral Images on Poultry Carcasses, Journal of Agricultural Engineering Research, Volume 78, Issue 2, Pages 127-139, 2001. https://doi.org/10.1006/jaer.2000.0658
  19. A. Shamsheyeva, A. Shamsheyeva, The Anisotropic Gaussian Kernel for SVM classification of HRCT images of the lung, in: Intelligent Sensors, Sensor Networks and Information Processing Conference, pp. 439-444, 2004.
  20. R. F. Chang, W. J Wu, W. K. Moon, Y. H Chou and D. R. Chen, Support Vector Machines for Diagnosis of Breast Tumors on US Images, Academic Radiology, Vol. 10, pp. 189-197, 2003. https://doi.org/10.1016/S1076-6332(03)80044-2