Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Genetic lesion matching algorithm using medical image

의료영상 이미지를 이용한 유전병변 정합 알고리즘

  • Cho, Young-bok (Department of Computer Science, Chungbuk National University) ;
  • Woo, Sung-Hee (Department of Medical Information IT&Engineering, Korea National University of Transportation) ;
  • Lee, Sang-Ho (Department of Computer Science, Chungbuk National University) ;
  • Han, Chang-Su (Department of R&D Center, SONOUM Inc)
  • Received : 2016.12.13
  • Accepted : 2017.01.25
  • Published : 2017.05.31
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Abstract

In this paper, we proposed an algorithm that can extract lesion by inputting a medical image. Feature points are extracted using SIFT algorithm to extract genetic training of medical image. To increase the intensity of the feature points, the input image and that raining image are matched using vector similarity and the lesion is extracted. The vector similarity match can quickly lead to lesions. Since the direction vector is generated from the local feature point pair, the direction itself only shows the local feature, but it has the advantage of comparing the similarity between the other vectors existing between the two images and expanding to the global feature. The experimental results show that the lesion matching error rate is 1.02% and the processing speed is improved by about 40% compared to the case of not using the feature point intensity information.

제안 논문에서는 의료영상 이미지를 입력받아 병변 추출이 가능한 알고리즘을 제안한다. 의료영상 이미지의 병변을 추출하기 위해 SIFT 알고리즘을 이용해 특징점들을 추출한다. 특징점의 강도를 높이기 위해 벡터 유사도를 이용해 입력 영상과 병변이미지를 정합하고 병변을 추출한다. 벡터 유사도 정합을 통해 빠르게 병변을 도출할 수 있다. 국소적인 특징점 쌍으로부터 방향 벡터를 생성하기 때문에 방향 자체는 국소적인 특징만을 나타내지만 두 영상 간에 존재하는 다른 벡터들 간의 유사도를 비교하고 전역적인 특징으로 확장될 수 있는 장점을 갖는다. 또한 병변 정합 오류율은 평균 1.02%, 처리속도는 특징점 강도 정보를 사용하지 않을 때보다 약 40%가 향상됨을 실험을 통해 보였다.

Keywords

References

  1. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, November 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  2. M. Brown and D. G. Lowe, "Invariant Features from Interest Point Groups", In Proceedings of the 13th British Machine Vision Conference, vol. 4, pp.253-262, 2002.
  3. D. G. Lowe, "Object Recognition from Local Scale-Invariant Features," in proceedings of the seventh IEEE international conference on, Greece, vol. 2, pp.1150-1157, 1999.
  4. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Online Finger-knuckle-print Verification for Personal Authentication," International Journal of Pattern Recognition, vol. 43, no. 7, pp. 2560-2571, July 2010. https://doi.org/10.1016/j.patcog.2010.01.020
  5. L. Zhang, L. Zhang, and D. Zhang, "Monogenic Code: A Novel Fast Feature Coding Algorithm with Applications to Finger Knuckle-Print Recognition," in Proceedings of I Emerging Techniques and Challenges for Hand-Based Biometrics(ETCHB) 2010 International Workshop on IEEE, pp. 1-4, 2010.
  6. M. Brown and G. L. David, "Automatic Panoramic Image Stitching using Invariant Features," International Journal of Computer Vision, vol. 74, no. 1, pp. 59-73, August 2007. https://doi.org/10.1007/s11263-006-0002-3
  7. M. Brown, R. Szeliski, and S. Winder, "Multi-image matching using multi-scale oriented patches," in Proceeding Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. vol. 1, 2005.
  8. H. Bay, A. Ess, T. Tinne, and L. V. Gool, "Speeded-Up Robust Features (SURF)," International Journal of Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, June 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  9. Z. Li, K. Wang, and W. Zuo, "Finger-Knuckle Print Recognition using Local Orientation Feature Based on Steerable Filter," in Proceeding International Conference on Intelligent Computing. Springer Berlin Heidelberg, pp. 224-230, 2012.
  10. S. Aoyama, K. Ito, and T. Aoki, "Finger-knuckle-print Recognition using BLPOC based local block matching," In Proceedings of the Pattern Recognition(ACPR)2011 First Asian Conference on. IEEE, pp. 525-529, 2011.
  11. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Ensemble of Local and Global Information for Finger-knuckle-print Recognition," International Journal of Pattern Recognition, vol. 44, no. 9, pp. 1990-1998, September 2011. https://doi.org/10.1016/j.patcog.2010.06.007