Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Computer-Aided Diagnosis a using Texture Feature Analysis Algorithm in Breast US images

유방 초음파영상에서 질감특성분석 알고리즘을 이용한 컴퓨터보조진단의 적용

  • Lee, Jin-Soo (Department of Radiology, Inje University Heaundae Paik Hospital) ;
  • Kim, Changsoo (Department of Radiological, College of Health Sciences, Catholic University of Pusan)
  • 이진수 (인제대학교 해운대백병원 영상의학과) ;
  • 김창수 (부산가톨릭대학교 보건과학대학 방사선학과)
  • Received : 2014.08.07
  • Accepted : 2015.01.08
  • Published : 2015.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper suggests 6 cases of TFA parameters algorithm(Mean, VA, RS, SKEW, UN, EN) to search for the detection of recognition rates regarding breast disease using CAD on ultrasound images. Of the patients who visited a university hospital in Busan city from August 2013 to January 2014, 90 cases of breast ultrasound images based on the findings in breast US and pathology were selected. $50{\times}50$ pixel size ROI was selected from the breast US images. After pre-processing histogram equalization of the acquired test images(negative, benign, malignancy), we calculated results of TFA algorithm using MATLAB. As a result, in the TFA parameters suggested, the disease recognition rates for negative and malignancy was as high as 100%, and negative and benign was approximately 83~96% for the Mean, SKEW, UN, and EN. Therefore, there is the possibility of auto diagnosis as a pre-processing step for a screening test on breast disease. A additional study of the suggested algorithm and the responsibility and reproducibility for various clinical cases will determine the practical CAD and it might be possible to apply this technique to range of ultrasound images.

본 연구는 초음파영상에서 컴퓨터보조진단으로 유방질환의 병변인식률을 알아보고자 6가지 질감특성분석 파라미터(평균밝기, 대조도, 평탄도, 왜곡도, 균일도, 엔트로피) 알고리즘을 제안하였다. 2013년 8월에서 2014년 1월까지 부산소재 대학병원을 내원한 환자 중 영상의학과 전문의의 판독과 세포병리학 진단 결과를 토대로 한 90증례의 유방 초음파영상을 대상으로 하였다. 연구방법은 유방 초음파영상에서 관심영역을 $50{\times}50$ 픽셀 크기로 설정하였으며, 획득된 실험영상(정상, 양성, 악성)에 히스토그램 평활화의 전처리 과정 후 MATLAB을 이용한 질감특성분석 알고리즘의 결과값을 산출하였다. 그 결과 제안된 질감특성분석 파라미터 중 평균밝기, 왜곡도, 균일도, 엔트로피의 정상과 악성의 병변인식률은 100%로 높게 나타났으며. 정상과 양성의 병변인식률은 약 83~96%를 나타내었다. 이러한 결과는 유방질환에서 감별진단의 전처리 단계로 자동진단의 가능성을 나타내며, 향후 제안된 알고리즘의 추가적인 연구와 다양한 임상증례에 대한 신뢰성과 재현성이 제공된다면 컴퓨터보조진단의 실용화기반을 마련할 수 있을 것이고, 다양한 초음파 영상에 대한 적용이 가능할 것으로 사료된다.

Keywords

References

  1. National Cancer Information Center, "htt://www.cancer.go.kr/mbs/cancer/subview.jspid, 2011.
  2. H. J. Yoon, M. H. Kim, Y. H. Choi, "Effective Computer-Aided Diagnosis Analysis for the Plaque Measurement on the Ultrasound image of the Carotid Artery", J Korean Soc. Ultrasound in Medicine, 23, 2, pp.105-111, 2004.
  3. S. H. Choi, S. Y. Chung, W. K. Lee, I. K. Yang, H. D Kim, J. S. Shin, B. H. Jung, W. J. Shin, H. H. Kim, S. H. Kim, "Ultrasonography in Paget's Disease of Breast:Comparison with Mammographic Finding", J Korean Soc. Ultrasound in Medicine, 20, 2, pp.137-142, 2001.
  4. Y. W. Sun, Y. J. Song, H. Y. Yun, D. H. Ryu, "Management fo Breast Masses Detected Only by Ultrasonography" Journal of Breast Cancer, 7, 1, pp.43-48, 2004.
  5. P. H. Arger, C. M. Sehgal, E, F. Conant, J. Zukerman, S. E. Rowling, J. A. Patton. "Interreader variability and predictive value of US descriptions of solid breast masses: pilot study", Acad Radiol, 8, 4, pp.335-342, 2001. https://doi.org/10.1016/S1076-6332(03)80503-2
  6. E. H. Lee, J. H. Cha, B. J. Cho, Y. H. Koh, B. J. Youn, W. K. Moon "Breast Imaging Reporting and Data System9BI-RADS) US leexion and Final Assesment Category for Solid Breast Masses: the Rates of Inter-and Intraobserver Agreement" J. Korean Soc. Radiology, 56, 6, pp.593-601, 2007. https://doi.org/10.3348/jkrs.2007.56.6.593
  7. M. R. De Mello, D. M. Albuquerque, F. G. Pereira-Cunha, K. B. Albanez, K. B. Pagnano, F. F. Costa, K. Metze, I. Lorand-Metze, "Molecular characteristics and chromatin texture features in acute promyelotic leukemia", Diagn Pathol, 28, 7, pp.75, 2012.
  8. H. S. Choi, "A Study on the Multi-View Based Computer Aided Diagnosis and 3-Dimentional Display System" The graduate school of Hanyang University, 2007.
  9. J. S. Lee, "Detection of Microcalcification using Computer Aided Diagnosis in the Breast US" The graduate school of Catholic University of Pusan, 2011.
  10. C. S. Kim, S. J. Ko, S. S. Kang, J. H. Kim, D. H. Kim, S. Y. Choi, "Computer-Aided Diagnosis for Liver Cirrhosis using Texture Features Information Analysis in Computed Tomography", Journal of the Korea Contents Association, 12, 4, pp.358-366, 2012. https://doi.org/10.5392/JKCA.2012.12.04.358
  11. J. S. Cho, H. S. Kang, H. S. Kim, S. D. Kim, "Multimedia signal processing: fundamentals and practice", 2nd edition, sungjin media, 2011.
  12. H. H. Park, " A Study of Recognition for Lung Cancer using Principle Component Analysis in Chest Radiography", The graduate school of Catholic University of Pusan, 2009.
  13. I. Christoyianni, A. Koutras, E. Dermatas, G. Kokkinakis, "Computer aided diagnosis of breast cancer in digitized Mammograms", Computerized Medical Imaging and Graphics, 26, 54, pp.309-319, 2002. https://doi.org/10.1016/S0895-6111(02)00031-9
  14. R. C. Gonzalez, R. E. Woods, S. L. Eddins, "Digital Image Processing using MATLAB", Prentice Hall, 2004.
  15. M. Gletsos, S. G. Mougiakakou, K. S. Nikita, A. S. Nikita, D. Kelekis, "A computer-aided diagnosis system to characterize CT focal liver lesion: design and optimization of a neural network classifier", IEEE Trans Inf Technol Biomed, 7, 3, pp.153-162, 2003. https://doi.org/10.1109/TITB.2003.813793
  16. M. A. Heller, "Texture perception in sight and blind observers", Percept Psychophs, 45, 1, pp.49-54, 1989. https://doi.org/10.3758/BF03208032
  17. D. Kontos, L. C. Ikejimba, P. R. Bakic, A. B. Troxel, E. F. Conant, A. D. Maidment, "Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment", Radiology, 261, 1, pp.80-91, 2011. https://doi.org/10.1148/radiol.11100966
  18. X. J. Chen, D. Eu, Y. He, S. Liu, "Study on application of multi-spectral image texture to discriminating rice categories base on wavlet packet and support vector machine", Guang Pu Xue Yu Guang Pu Fen Xi., 29, 1, pp.222-225, 2009.
  19. D. H. Kim, S. J. Ko, S. S. Kang, J. H. Kim, C. S. Kim, "Computer-Aided Diagnosis for Pulmonary Tuberculosis using Texture Features Analysis in Digital Chest Radiography", Journal of the Korea Contents Association, 11, 11, pp.185-193, 2011. https://doi.org/10.5392/JKCA.2011.11.11.185
  20. S. J. Ko, J. S. Lee, S. Y. Ye, C. S. Kim, "Application of Texture Features Algorithm using Computer-Aided Diagnosis of Papillary Thyroid Cancer in the Ultrasonography", Journal of the Korea Contents Association, 13, 5, pp.303-310, 2013. https://doi.org/10.5392/JKCA.2013.13.05.303
  21. J. E. Yoo, T. S. Jun, J. Y. Jeong, I. C. Im, J. S. Lee, H. H. Park, "Application of Texture Feature Analysis Algorithm used the Statistical Charaterristics in the Computed Tomography: A base on the Hepatocellular Carcinoma(HCC)", J. Korean Soc. Radiology, 7, 1, pp.9-15, 2013. https://doi.org/10.7742/jksr.2013.7.1.009
  22. S. J. Kim, N. R. Y. Cho, J. H. Cha, H. K. Jeong, S. H. Lee, K. S. Cho, S. M. Kim, Y. K. Moon, "Reprducibility of Computer-Aided Detection System in Digital Mammograms", J. Korean Soc. Radiology, 52, 2, pp.137-142, 2005. https://doi.org/10.3348/jkrs.2005.52.2.137
  23. J. W. Back, "The usefulness and Limitation of Breast Ultrasonography" The graduate school of Korea University, 2011.

Cited by

  1. Image Analysis of Diffuse Liver Disease using Computer-Adided Diagnosis in the Liver US Image vol.9, pp.4, 2015, https://doi.org/10.7742/jksr.2015.9.4.227