Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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MRI Data Segmentation Using Fuzzy C-Mean Algorithm with Intuition

직관적 퍼지 C-평균 모델을 이용한 자기 공명 영상 분할

  • 김태현 (명지대학교 전자공학과) ;
  • 박동철 (명지대학교 전자공학과) ;
  • 정태경 (명지대학교 전자공학과) ;
  • 이윤식 (전자부품연구원 시스템반도체연구본부) ;
  • 민수영 (전자부품연구원 시스템반도체연구본부)
  • Received : 2011.06.27
  • Accepted : 2011.08.04
  • Published : 2011.09.30
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Abstract

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

직관적 퍼지 c-평균 군집화 모델을 이용하는 자기공명 영상의 분할 방법이 본 논문에서 제안되었다. 본 논문에서 채택하는 fuzzy c-means with intuition (FCM-I)은 잡음의 영향을 줄이기 위하여 직관이라는 척도를 사용한다. 실제적 자기 공명 영상에 대해 영상 분할의 실험을 수행하고 기존의 몇몇 군집화 알고리즘과 성능을 비교하였다. 기존의 모델들과 성능을 비교한 결과, FCM-I 기반의 분할 방법은 잡음과 필요한 계수의 선택에 대해 상대적으로 강인하여, 영상 분할에 유용한 모델이 될 수 있음을 확인할 수 있었다.

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References

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