산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제31권1호
  • /
  • Pages.74-82
  • /
  • 2008
  • /
  • 2005-0461(pISSN)
  • /
  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
권호 표지

유방 초음파 영상에서 도메인 경험 지식 기반의 노이즈 필터링 알고리즘을 이용한 ROI(Region Of Interest) 추출

The Extraction of ROI(Region Of Interest)s Using Noise Filtering Algorithm Based on Domain Heuristic Knowledge in Breast Ultrasound Image

  • 구락조 (아주대학교 산업공학과) ;
  • 정인성 (아주대학교 산업정보시스템 공학부) ;
  • 최성욱 (아주대학교 산업공학과) ;
  • 박희붕 (박희붕외과 유방클리닉) ;
  • 왕지남 (아주대학교 산업정보시스템 공학부)
  • Koo, Lock-Jo (Dept. of Industrial Engineering in Ajou University) ;
  • Jung, In-Sung (Div. of Industrial and Information System Engineering in Ajou University) ;
  • Choi, Sung-Wook (Dept. of Industrial Engineering in Ajou University) ;
  • Park, Hee-Boong (Park Breast Clinic) ;
  • Wang, Gi-Nam (Div. of Industrial and Information System Engineering in Ajou University)
  • 발행 : 2008.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this paper is to remove noises of image based on the heuristic noises filter and to extract a tumor region by using morphology techniques in breast ultrasound image. Similar objective studies have been conducted based on ultrasound image of high resolution. As a result, efficiency of noise removal is not fine enough for low resolution image. Moreover, when ultrasound image has multiple tumors, the extraction of ROI (Region Of Interest) is not accomplished or processed by a manual selection. In this paper, our method is done 4 kinds of process for noises removal and the extraction of ROI for solving problems of restrictive automated segmentation. First process is that pixel value is acquired as matrix type. Second process is a image preprocessing phase that is aimed to maximize a contrast of image and prevent a leak of personal information. In next process, the heuristic noise filter that is based on opinion of medical specialist is applied to remove noises. The last process is to extract a tumor region by using morphology techniques. As a result, the noise is effectively eliminated in all images and a extraction of tumor regions is possible though one ultrasound image has several tumors.

키워드

참고문헌

  1. 김용선, 이덕운, 나종범; '2차원 초츰파 B모드 영상의 주관절 화질 개선', 제17회 영상처리 및 이해에 관한 워크샵 발표 논문집, 2005
  2. 류기선, '한국인 유방암의 특징', 방사선 기술 과학, 26(1)：16-21, 2003
  3. 보건 복지부, '암 통계', 2007, http://211.114.9.232/nci apps/user/basicinfo/gcancer_main.jsp
  4. 윤현주, 김명희, 최연현, '초음파 이미지에서의 경동맥 내 플라크 측정을 위한 효과적인 컴퓨터 보조 기법', 대한 초음파의학회지, 23：105-111, 2004
  5. 이필규, '영상처리 및 생체인식', 흥릉과학, 2005
  6. 정문기, '내시경 초음파 기기 및 원리', 대한소화기 내시경학회지, 29：27-42, 2004
  7. 정수영, 이경원, 양익, 김홍대, 신상준, 정봉화, 심정원, 김학회, 김현숙, '유방 파젯트병에서 유방 초음파 검사의 유용성：유방 촬영술 소견과의 비교,' 대한 초음파의학회지, 20：137-142, 2001
  8. 정인성, 구락조, 왕지남, '초음파를 이용한 진단 및 자동 분할 및 진단 알고리즘', 인간공학/HCI, 추계학술대회 논문집, 2004
  9. Madabhushi, A. and Metaxas, D. N.; 'Auotmatic boundary extraction of ultrasonic breast leisons,' EEE International Symposium on Biomedical Imaging：601-604, 2002
  10. Madabhushi, A. and Metaxas, D. N., 'Combining Low-, High-Level and Empirical Domain Knowledge for Automated Segmentation of Ultrasonic Breast Lesions,' IEEE Transactions on Medical Imaging, 22(2), 2003
  11. Yap, M. H., Edirisinghe, E. A., and Helmut, E. Bez; 'Object Boundary Detection in Ultrasound Images,' Computer and Robot Vision, The 3rd Canadian Conference on, 2006
  12. Lim, Jae S.; 'Two-Dimensional Signal and Image Processing,' Englewood Cliffs, NJ：Prentice Hall： 536-540, 1990
  13. Paulo, S. Rodrigues, Gilson A. Giraldi, Marcia Provenzano, Marcelo D. Faria, Ruey-Feng Chang, and Jasjit S. Suri; 'A New Methodology Based on q-Entropy for Breast Lesion Classification in 3-D Ultrasound Images,' Proceedings of the 28th IEEE EMBS Annual International Conference, 2006
  14. Perona, P. and Malik, J.; 'Scale-Space and Edge Detection Using Anisotropic Diffusion,' IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 1990
  15. Chang, R. F., Wu, W. J., Tseng, C. C., Chen, D. R., and Moon, W. K.; '3-D Snake for US in Margin Evaluation for Malignant Breast Tumor Excision Using Mammotome,' IEEE Transactions on Information Technology in Biomedicine, 7(3), 2003
  16. Samuel, K. Moore; 'Better Breast Cancer Detection,' IEEE SPECTRUM, 38：50-54, 2001 https://doi.org/10.1109/6.920031
  17. Green W. B., 'Digital Image Processing-A System Approach,' Van Nostrand Reinhold：69-72, 1989
  18. Xu, Liu, Zhimin Huo, and Jiwu Zhang, 'Automated segmentation of breast lesions in ultrasound images,' 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005
  19. Huang, Y. L. and Chen, D. R., 'Automatic Contouring for Breast Tumors in 2-D Sonography,' Annual International Conference of the Engineering in Medicine and Biology Society, 2005