Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Detection of Mass on Dense Mammogram

고밀도 유방영상에서 종양의 추출

  • 유승화 (충남대학교 공과대학 정보통신공학과) ;
  • 노승무 (충남대학교 의과대학 일반외과) ;
  • 박종원 (충남대학교 공과대학 정보통신공학과)
  • Published : 2001.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposed automated methods for the detection of breast mass. We analysed characteristic of the mass by using the features on mammograms. The homogeneity was used to distinguish mass and abnormal homogeneous tissue from the Cooper's ligament and multiple threshold method was used to deal with the high density candidates. By using the 8-connectivity, the first step candidates were selected. We generated the dualistic images of each candidate in which we regard the gray value as topographic height information. From these candidates, the second candidates were selected by comparing the circularity and the distribution rates. The final detection was done with the method in which we generated the template of each candidate and compared each other. From these methods, we grade the order from the candidate. We applied the algorithm to the 136 mammograms and compared to the radiologist's outlines of the leisions. The detection resulted that the sensitivity of the proposed methods was 93.38% and 97.63% FP(False positive) which we can segmented mass in the first grade in the 124 cases.

제안된 연구는 유방촬영영상(Mammogram)에서 종양의 추출에 관한 연구로서, 맘모그램의 특성을 파악하여 종양에 대한 자동적인 추출을 시행하였다. 처리과정에서 동질성 특성을 이용하여 정상조직인 Cooper's ligament로부터 종양조직을 분리하였고 고밀도 후보에 대한 처리방법으로 다중 문턱값 적용방식을 사용하였다. 추출된 부분을 8-연결성 관계를 사용하여 1차 후보를 추출하였다. 1차 추출된 각 후보에 대하여 명암값을 지형적 높이정보로 해석한 이분화영상으로 표현하여 이중원형성과 분포 비율을 비교하는 방법을 통하여 2차 후보 추출을 시행하였다. 최종적인 종양의 결정은 공간원형성 판단을 위한 반구 형태의 템플리트를 생성하여 비교하는 방법을 이용하여 후보에 대한 순위를 결정하였다. 알고리즘을 실제 종양이 확진된 환자의 136 예에 적용하여 추출된 결과와 전문의가 지적한 결과를 비교하여 93.38%의 민감도를 얻었으며, 최종추출 단계에서는 124 예에서 1 순위로 종양을 추출하여 97.63%의 FP(False positive)의 결과를 나타냈다.

Keywords

References

  1. W. E. Polakowski, D. A. Cournoyer, S. K. Rogers, M. P. DeSimio, D..W. Ruck, J. W. Hoffmeister, and R. A. Raines, ComputerAided Breast Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency, IEEE Trans. on Med Imaging, vol. 16, No.5, pp. 811-819, Dec.l997 https://doi.org/10.1109/42.650877
  2. G. M. Brake, N. Karssemeijer, Single and Multiscale Detection of Masses in Digital Mammograms, IEEE Trans on Med Imaging, vol.18, No.7 pp. 628-639, Jul.1999 https://doi.org/10.1109/42.790462
  3. N. Petrick, H. P. Chan, B.Sahiner and D. Wei, An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection, IEEE Trans on Med Imaging, vol.15, No.1, pp. 59-67, Feb. 1996 https://doi.org/10.1109/42.481441
  4. N. Petrick, H. P. Chan, B. Sahiner and M. A. Helvie, Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms, Med.Phys, vol.26, No.8, pp.1642-1654, Aug. 1999 https://doi.org/10.1118/1.598658
  5. H. Kobatake, M. Murakami, H. Takeo and S. Nawano, Computerized Detection of Malignant Tumors on Digital Mammograms, IEEE Trans on Med Imaging, vol.18, No.5, pp. 369-378, May. 1997 https://doi.org/10.1109/42.774164
  6. M. A. Kupinski and M. L. Giger, Automated Seeded Lesion Segmentation on Digital Mammograms, IEEE Trans on Med Imaging, vol.17, No.4, pp. 510-517, Aug.l998 https://doi.org/10.1109/42.730396
  7. A. J. Mndez, P. G. Tahoces, M. J. Lado, M. Souto and J. J. Vidal, Computer-aided diagnosis : Automatic detection of malignant masses in digitized mammograms, Med.Phys, vol.25, No.6, pp.957-964, Jun.l998 https://doi.org/10.1118/1.598274
  8. D. Wei, H..P. Chan, N. Petrick, B. Sahiner, M. A. Helvie, D. D. Adler and M. M. Goodsitt, False-positive reduction technique for detection of masses on digital mammograms : Global and local multiresolution texture analysis, Med.Phys, vol.24, No.6, pp.903-914, Jun.1997 https://doi.org/10.1118/1.598011
  9. H. P Chan, D. Wei, M. A. Helvie, B. Sahiner, D. D. Adler, M. M. Goodsitt and.N. Petrick, Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space, Phys.Med.Biol, vol.40, No.5, pp.857-876, 1995 https://doi.org/10.1088/0031-9155/40/5/010
  10. J. K. Kim, J. M. Park, K. S. Song and H. W. Park, Adaptive Mammographic Image Enhancement Using First Derivative and Local Statistics, IEEE, Trans on Med Imaging, vol.16, No.5 pp, 495-502, Oct.l997 https://doi.org/10.1109/42.640739
  11. B. Sahiner, H. P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler and M. M. Goodsitt, Classification of Mass and Normal Breast Tissue: A Convolution Neural Network Classifier with Spatial Domain and Texture Images, IEEE Trans on Med Imaging, vol.15, No.5, pp. 598-610, Oct.1996 https://doi.org/10.1109/42.538937
  12. L. M. Bruce and R. R. Adhami, Classifying Mammographic Mass Shapes Using the Wavelet Transform Modulus-Maxima Method, IEEE Trans on Med Imaging, vol.18, No.12, pp.1170-1177, Dec. 1999 https://doi.org/10.1109/42.819326
  13. L. Li, W. Qian and L. P. Clarke, Digital Mammography: Computer-assisted Diagnosis Method for Mass Detection with Multi-orientation and Multiresolution Wavelet Transforms, Acad Radiol, vol.4, No.11, pp. 724-731, Nov.1997 https://doi.org/10.1016/S1076-6332(97)80075-X
  14. R. M. Rangayyan, N. M. El-Faramawy, J. E. Leo Desautels and O. A. Alim, Measures of Acutance and Shape for Classification of Breast Tumors, IEEE Trans on Med Imaging, vol.16, No.6, pp. 799-810, Dec. 1997 https://doi.org/10.1109/42.650876
  15. B. Sahiner, H. P. Chan, D. Wei, N. Petrick, M. A. Helvie, D. D. Adler and M. M. Goodsitt, Image feature selection by a genetic algorithm : Application to classification of mass and normal breast tissue, Med.Phys, vol.23, No.10, pp.1671-1684, Act.l996 https://doi.org/10.1118/1.597829
  16. H. P. Chan, B. Sahiner, M. A. Helvie, N. Petrick, M. A. Roubidoux, T. E. Wilson, D. D. Adler, C. Paramagul, J. S. Newman and S. Sanjay-Gopal, Improvement of Radiologists Characterization of Mammographic Masses by Using Computer-aided Diagnosis : An ROC Study,' Radiology, vol.212, No.3, pp.817-827, Sep.l999
  17. B. Sahiner, H. P. Chan, N. Petrick, M. A. Helvie and M. M. Goodsitt, Computerized characterization of masses on mammograms : The rubber band straightening transform and texture analysis, Med.Phys, vol.25, No.4, pp. 516-526, Apr.1998 https://doi.org/10.1118/1.598228
  18. H. Kobatake and Y. Yoshinaga, Detection of Spicules on Mammogram Based on Skeleton Analysis, IEEE Trans on Med Imaging, vol.15, No.3 pp, 235-245, Jun.l996 https://doi.org/10.1109/42.500062
  19. C. J. Vyborny, T. Doi, K. F. O'Shaughnessy, H. M. Romsdahl, A. C. Schneider and A. A. Stein, 'Breast Cancer:Importance of Spiculation in Computer-aided Detection', Radiology, vol.215, No.3, pp.703-707, Jun.2000
  20. 한국중앙암등록본부, 한국중앙암 등록 사업연례보고서 1997.1-1997.12. 보건복지부, 1999
  21. Y.O. Ahn, B. J. Park, K. Y. Yoo, et al. 'Incidence estimation of female breast cancer among Koreans, J Korean Med Sci. vol. 9, pp. 328-333, 1994
  22. C. L. Kosany, LAG Ries, B. A. Miller, A. Harras, B. K. Edwards(eds), SEER Cancer Statistics Review, 1973-1992, Tables and Graphs, National Cancer Ins., NIH pub. No. 95-2789, Bethesdaaa, MD,1995
  23. L. H. Barker, Breast cancer detection demonstration project : Fiveyear summary report, CA Cancer J Clin, vol. 32, No.4, pp. 194-225, 1994
  24. 오기근, 이경식, 정우희, 병합방사선 진단방법을 이용한 한국인 여성 유방암의 연구, 대한방사선의학회지, vol. 22, pp. 743-760, 1986
  25. A. P. Pentland, 'Fractal-Based Description of Natural Scenes', IEEE Trans. Pattern Anal. Machine Intell. Vol. PAMI-6, No.6, Nov. 1984
  26. W. E. Higgins and E. J. Ojard, Interactive Morphological Watershed Analysis for 3D Medical Images, Computerized Medical Imaging and Graphics, vol. 17, pp387-395, 1993 https://doi.org/10.1016/0895-6111(93)90033-J
  27. American College of Radiology, Illustrated Breast Imaging Reporting and Data system (BI-RADSTM), 3rd ed. Reston(VA), American College of Radiology, 1998
  28. J. R. Parker, Algorithms for Image Processing and Computer Vision, John Wiley & Sons, Inc. 1997