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Background Removal from XRF Spectrum using the Interval Partitioning and Classifying

구간 분할과 영역 분류를 이용한 XRF 스펙트럼의 백그라운드 제거

  • Yang, Sanghoon (Department of Electronic Engineering, Chonbuk National University) ;
  • Lee, Jaehwan (Department of Electronic Engineering, Chonbuk National University) ;
  • Yoon, Sook (Department of Multimedia Engineering, Mokpo National University) ;
  • Park, Dong Sun (IT Convergence Research Center, Chonbuk National University)
  • 양상훈 (전북대학교 전자공학과) ;
  • 이재환 (전북대학교 전자공학과) ;
  • 윤숙 (목포대학교 멀티미디어공학과) ;
  • 박동선 (전북대학교 IT융합연구센터)
  • Received : 2013.05.15
  • Published : 2013.09.25

Abstract

XRF spectrum data of a material include a lot of background signals which are not related to its components. Since an XRF analyzer analyzes components and concentrations of an analyte using the locations and magnitudes of gaussian-shaped peaks extracted from a spectrum, its background signals need to be removed completely from the spectrum for the accurate analysis. Morphology-based method, SNIP-based method and thresholding-based method have been used to remove background signals. In the paper, a background removal method, an improved version of an interval-thresholding-based method, is proposed. The proposed method consists of interval partitioning, interval classifying, and background estimation. Experimental results show that the proposed method has better performance on background removal from the spectrum than the existing methods, morphology-based method and SNIP-based method.

X-선 스펙트럼 데이터는 물질의 성분과 관련이 없는 많은 양의 백그라운드 신호를 포함하고 있다. XRF는 스펙트럼에서 가우시안 형태의 피크 정보의 위치와 크기를 이용하여 시료의 성분과 함유량을 분석하며, 시료의 성분과 함유량을 정확히 분석하기 위해서는 백그라운드를 정확하게 제거하여야 한다. 백그라운드를 제거하기 위한 방법으로는 모폴로지, SNIP, 임계값 방법들이 적용되고 있으며, 구간별 임계값 방법에 대한 연구가 활발히 진행되었다. 본 논문에서 제안하는 백그라운드 제거 방법은 구간별 임계값 방법을 개선한 방법으로 구간분할과 영역분류, 백그라운드추정으로 구성된다. 제안된 방법이 백그라운드 제거에서 기존의 모폴로지 기반 방법이나 SNIP 방법에 비해 좋은 성능을 보임을 실험을 통해 검증하였다.

Keywords

References

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