Journal of the Korean Society of Radiology (한국방사선학회논문지)

The Korean Society of Radiology (한국방사선학회)
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Texture Feature analysis using Computed Tomography Imaging in Fatty Liver Disease Patients

Fatty Liver 환자의 컴퓨터단층촬영 영상을 이용한 질감특징분석

  • Park, Hyong-Hu (Department of Radiological Science, International University of Korea) ;
  • Park, Ji-Koon (Department of Radiological Science, International University of Korea) ;
  • Choi, Il-Hong (Department of Radiological Science, International University of Korea) ;
  • Kang, Sang-Sik (Department of Radiological Science, International University of Korea) ;
  • Noh, Si-Cheol (Department of Radiological Science, International University of Korea) ;
  • Jung, Bong-Jae (Department of Radiological Science, International University of Korea)
  • 박형후 (한국국제대학교 방사선학과) ;
  • 박지군 (한국국제대학교 방사선학과) ;
  • 최일홍 (한국국제대학교 방사선학과) ;
  • 강상식 (한국국제대학교 방사선학과) ;
  • 노시철 (한국국제대학교 방사선학과) ;
  • 정봉재 (한국국제대학교 방사선학과)
  • Received : 2016.01.14
  • Accepted : 2016.02.29
  • Published : 2016.02.29
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Abstract

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

본 실험에서 제안된 질감특징분석 알고리즘은 지방간 환자의 CT영상을 이용하여 정상영상과 질환영상으로 구분하여, 정상 간 CT영상과 지방간 CT영상을 생성하고 제안된 질감특징분석을 이용한 컴퓨터보조 진단 시스템에 적용하여 6개의 파라메타로 정량적 분석을 통해 지방간 CT영상의 질환 인식률을 도출하고 평가하였다. 결과로 지방간 CT영상 30증례 중에서 각각의 파라메타별 질감특징 값에 대한 인식률은 평균 밝기의 경우 100%, 엔트로피의 경우 96.67%, 왜곡도의 경우 93.33%로 높게 나타났고, 평탄도의 경우 83.33%, 균일도의 경우 86.67%, 평균대조도의 경우 80%로 다소 낮은 질환 인식률을 보였다. 따라서 본 연구의 결과를 바탕으로 의료영상의 컴퓨터보조진단 시스템으로 발전된 프로그램을 구현한다면 지방간 CT영상의 질환부위 자동검출 및 정량적 진단이 가능해 컴퓨터보조진단 자료로서 활용이 가능할 것으로 판단되며 최종판독에서 객관성, 정확성, 판독시간 단축에 유용하게 사용 될 것으로 사료된다.

Keywords

References

  1. Yalamanchili. K, Saadeh. S, Klintmalm. G. B, Jennings. L. W, Davis. G. L, "Nonalcoholic fatty liver disease after liver transplantation for cryptogenic cirrhosis or nonalcoholic fatty liver disease," Liver Transplantation, Vol. 16, No. 4, pp. 431-439, 2010. https://doi.org/10.1002/lt.22004
  2. R. N. Hardwick, C. D. Fisher, M. J. Canet, G. L. Scheffer, N. J. Cherrington, "Variations in ATP-Binding Cassette Transporter Regulation during the Progress ion of Human Nonalcoholic Fatty Liver Disease," Drug metabolism and disposition, Vol. 39, No. 12, pp. 2395-2402, 2011. https://doi.org/10.1124/dmd.111.041012
  3. A. E. Bohte, J. R. Werven, S. Bipat. J. Stoker, "The diagnostic accuracy of US, CT, MRI and 1H-MRS for the evaluation of hepatic steatosis compared with liver biopsy: a meta-analysis," European radiology, Vol. 21, No. 1, pp. 87-97, 2011. https://doi.org/10.1007/s00330-010-1905-5
  4. Z. Szucs. Farkas, M. A. Patak, S. Yuksel. Hatz, T. Ruder. P. Vock, "Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers," European radiology, Vol. 20, No. 6, pp. 1289-1296, 2010. https://doi.org/10.1007/s00330-009-1667-0
  5. B. Van. Ginneken, C. M. Schaefer. Prokop, M. Prokop, "Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic," Radiology, Vol. 261, No. 3, pp. 719-732, 2011. https://doi.org/10.1148/radiol.11091710
  6. M. Beil, T. Irinopoulou, J. Vassy, Rigaut. J. P, "Chromatin Texture Analysis in Three-Dimensional Images from Confocal Scanning Laser Microscopy," Analytical and quantitative cytology and histology, Vol. 17, No. 5, pp. 323, 1995.
  7. S. Selvan, S. Ramakrishnan, "SVD-Based Modeling for Image Texture Classification Using Wavelet Trans formation," IEEE transactions on image processing, Vol. 16, No. 11, pp. 2688-2696, 2007. https://doi.org/10.1109/TIP.2007.908082
  8. El. Yazaji. M, Battas. O, Agoub. M, Moussaoui. D, Gutknecht. C, Dalery. J, d'Amato. T, Saoud. M, "Validity of the depressive dimension extracted from principal component analysis of the PANSS in drug-free patients with schizophrenia," Schizophr Research, Vol. 56, pp. 121-127, 2002. https://doi.org/10.1016/S0920-9964(01)00247-X
  9. J. Yu, J. Tan, "Object density-based image segmentation and its applications in biomedical image analysis," computer methods and programs in biomedicine, Vol. 96, No. 3, pp. 193-204, 2009. https://doi.org/10.1016/j.cmpb.2009.04.013
  10. Gletsos. M, Mougiakakou. S. G, Matsopoulos. G. K, Nikita. K. S, Nikita. A. S, Kelekis. D, "A computeraided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier," IEEE Trans Inf Technol Biomed, Vol. 7, pp. 153-162, 2003. https://doi.org/10.1109/TITB.2003.813793
  11. Alshamlan. H, El-Zaart. A, "Breast Cancer Computer Aided Diagnosis (CAD) System," proceedings of the international conference on image processing computer vision and pattern recognition, Vol. 1, pp. 224-229, 2011.
  12. Chen. X. J, Wu. D, He. Y, Liu. S, "Study on application of multi-spectral image texture to discriminating rice categories based on wavelet packet and support vector machine," Guang Pu Xue Yu Guang Pu Fen Xi, Vol. 29, pp. 222-225, 2009.