치밀 유방영상에서 mass형 유방암 자동 검출

Automatic detection of mass type - Breast cancer on dense mammographic images

  • Chon Min-Su (Department of Electronics Engineering, Kyung Hee University) ;
  • Park Jun-Young (Department of Electronics Engineering, Kyung Hee University) ;
  • Kim Won-Ha (Department of Electronics Engineering, Kyung Hee University)
  • 발행 : 2006.09.01

초록

본 논문에서는 치밀 유방영상에서 mass형 암 검출을 목적으로 하는 시스템을 개발한다. 본 논문에서 제시하는 방법과 기존의 방법과의 차이점은 1) mass 영역의 중심의 위치와 반경을 영상신호의 불규칙성에 영향을 받지 않고 안정적으로 결정하는 방법을 제시하고, 2) mass형 유방암 영상에 적용하기 적합한 방사형 필터를 개발하며, 3) mass형 유방암 검출을 위해 mass 경계선의 불규칙성, mass 영역 중심부의 homogeneity, mass 영역의 이심율에 근거하여 다중 특징 함수 개발에 있다. 본 논문에서 제안한 시스템은 기존의 시스템보다 치밀 유방에 적용하였을 때 false alarm은 영상 당 1개 정도 높으나 true alarm 비율은 10%이상 향상 되었다.

In this paper we developed a novel system for automatic detection of mass type breast cancer on dense digital mammogram images. The new approaches presented in this paper are as follows: 1) we presented a method that stably decides the mass center and radius without being affected by image signal irregularity. 2) We developed a radial directional filter that is suitable to process mass image signal. 3) And we developed the multiple feature function based on mass shape spiculation, mass center homogeneity, and mass eccentricity, so as to determine mass-type breast cancer. When the proposed system is applied to dense mammographic images, the true 기arm rate is improved by 10% over a conventional system while the false alarm is increased by 1 per image.

키워드

참고문헌

  1. R. M. Rangayyan, Biomedical Analysis, CRC press, 2005
  2. N. Karssemeijer, 'Detection of masses in mammogram Image Processing Techniques for Tumor Detection (ed, Strickland, R. N.)', Marcel Dekker, Inc., pp, 187-212, 2002
  3. Brake, G. M, Karssemeijer, N. , 'Single and multiscale detection of masses in digital mammograms', IEEE Trans. Medical Imaging, vol. 18, no. 7, July (1999) 628-639 https://doi.org/10.1109/42.790462
  4. Huang, S., Chang, R., Chen, D., Moon, W.: Characterization of spiculation on ultrasound lesions. IEEE Trans. Medical Imaging, vol. 23, no. 1, Jan. (2004) 111-121 https://doi.org/10.1109/TMI.2003.819918
  5. L.F. Costa and R. M. Cesar, Shape Analysis and Classification, CRC Press, 2001
  6. J. Gibson and K. Syood, 'Lattice quantization', Advanced Electronic Physics, Vol.72, pp.259-330, June 1988
  7. HyungJun Kim, WonHa Kim, AutoMatic Detection of Spiculated Massess Using Fractal Analysis in Digital Mammography, LNCS 3691, Computer Analysis of Images and Patterns, pp256- 263, 2005 https://doi.org/10.1007/11556121_32
  8. N. Otsu, 'A threshold selection method from gray-level histograms', IEEE Trans. Syst, Man Cybern, vol. SMC-9, pp. 62-66, Jan. 1979
  9. A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1899
  10. R. Jain, R. Kasturi and B. Schunck, Machin Vision, McGraw Hill, 1995