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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Algorithm for Extract Region of Interest Using Fast Binary Image Processing

고속 이진화 영상처리를 이용한 관심영역 추출 알고리즘

  • Cho, Young-bok (Department of Computer & Information Security, Daejeon University) ;
  • Woo, Sung-hee (Department of Medical IT&Engineering, Korea National University of Transportation)
  • Received : 2017.11.21
  • Accepted : 2017.12.14
  • Published : 2018.04.30
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Abstract

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

본 논문에서는 방사선 영상을 기반으로 관심 영역의 자동 추출 알고리즘을 제안한다. 제안 알고리즘은 입력 영상에서 병변부위를 검출하기 위해 세그먼테이션, 특징 추출 및 참조 이미지 매칭을 이용한다. 추출된 영역은 참조 DB에서 일치하는 병변 이미지를 검색하고, 일치된 결과는 칼만 필터 기반의 적합성 피드백을 이용해 병변을 자동 추출한다. 제안 알고리즘은 왼손 x-ray 입력 영상을 기반으로 성장판을 추출하기 위해 왼손 이미지의 윤곽선을 추출하고, 이것은 다중 스케일 해시안 행렬 기반의 세션화를 이용해 후보 영역을 생성 한다. 그 결과, 제안 알고리즘은 관심영역 분할 단계에서는 0.02초로 빠른 분할이 가능하였고, 분할 영상을 기준으로 ROI 추출시 평균 0.53, 강화 단계에서는 0.49초로 매우 정확한 이미지 분할이 가능한 것을 실험을 통해 알 수 있었다.

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References

  1. H. J. Kang, "National-Level Use of Health Care Big Data and Its Policy Implications," International Journal of Health and welfare policy forum, vol.2384, no.6, pp. 55-71, Aug. 2016.
  2. P. Rakwatin, W. Takeuchi, and Y, Yasuoka, "Stripe noise reduction in MODIS data by combining histogram matching with facet filter", International Journal of the IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no.6, pp.1844-1856, June 2007. https://doi.org/10.1109/TGRS.2007.895841
  3. L. Molinari, T, Gasser, and R. H. Largo. "TW3 bone age: RUS/CB and gender differences of percentiles for score and score increments," International Journal of the Annals of human biology vol.31, no.4 pp.421-435, May 1997.
  4. M. J. Yaffe., and J. A. Rowlands. "X-ray detectors for digital radiography." International Journal of the Physics in Medicine and Biology, vol. 42, no.1, January 1997.
  5. Y. B. Cho. S. H Woo. S. H. Lee and C. S. Han, "Genetic lesion matching algorithm using medical image," Journal of the korea institute of information and communication engineering, vol 21, no.5, pp.960-966, May 2017. https://doi.org/10.6109/jkiice.2017.21.5.960
  6. 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
  7. 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, Sept. 2011. https://doi.org/10.1016/j.patcog.2010.06.007
  8. Y. B. Cho and S. H. Woo, "The Kirsch-Laplacian Edge Detection Algorithm for Predicting Iris-based Disease," In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design, pp.551-555, New Zealand. 2017.
  9. Y. B. Cho. S.H Woo. S. H. Lee and M. K. Kim, "An Efficient Approach in Iris Genetic Lesion Detection Algorithm using SURF and SVM," In Proceedings of the 2017 IEEE Signal Processing algorithms architectures arrangements and applications(SPA 2017) conference proceedings, pp.412-417, Poland. 2017.
  10. J. R. Kim, Y. S. Lee and Y.S. Yu, "Assessment of Bone Age in Prepubertal Healthy Korean Children: Comparison among the Korean Standard Bone Age Chart, Greulich-Pyle Method, and Tanner-Whitehouse Method," Journal of the Korean Journal of Radiology, vol.16, no.1, pp. 201-205, Jan. 2015. https://doi.org/10.3348/kjr.2015.16.1.201
  11. H. M. Sim, P. Ishvi, "Analysis of an Axial T2 Weighted Brain MRI," Asia-pacific Journal of Convergent Research Interchange, vol.3, no.1, pp. 45-55, March 2017.