DOI QR코드

DOI QR Code

Feature values of DWT using MR general imaging and molecular imaging

DWT를 이용한 MR 일반영상과 분자영상 특징추출

  • Received : 2012.08.26
  • Accepted : 2012.10.16
  • Published : 2012.10.30

Abstract

This study acquired molecular lmaging using nano-contrast agents, and the general condition of the same image acquisition to analyze the difference between molecular imaging and general imaging, two images are converted into DWT (Discrete Wavelet Transform). Nano-contrast agent imaging using MRI and molecular imaging using PET study of molecular imaging technology mainstream. DWT analysis of the same lesions using MRI imaging and molecular imaging block lesions are present in the lesions, illustrating the value of a high-frequency feature both highly general imaging and molecular imaging could know that. The high frequency region of the feature extraction values appear higher molecular imaging.

본 연구는 나노 조영제를 이용하여 분자영상을 획득하고 이와 동일한 조건의 일반영상을 획득하여 두 영상을 DWT(Discrete Wavelet Transform)로 변환하여 분자영상과 일반영상간의 차이를 분석하였다. 현재까지의 분자영상 기술은 나노 조영제를 이용한 MR 영상과, PET를 이용한 분자영상 연구가 주류를 이루고 있다. MRI를 이용한 동일병변의 일반영상과 분자영상을 DWT로 분석한 결과 병변이 존재하는 블록에서는 병변이 있음을 예시하여 주는 고주파 특징값이 일반영상과 분자영상 모두 더 높게 나타나는 것을 알 수 있었다. 특히 고주파 영역의 특징추출값은 분자영상이 더 높게 나타남을 알 수 있었다.

Keywords

References

  1. Mahwood U, Emerging Technologies That Will Change the World, Molecular Imaging. Tech Rev, 106, 2003..
  2. Chang, Thomas Ming Swi, Artifical cells, World Scientific Publishing. co. Pte. Lte, 2007.
  3. Byeong-Chole Ahn, Applications of molecular imaging in drug discovery and development process, Curr Pharm Biotechnol, Vol. 12, No. 4, pp.459-468, 2011. https://doi.org/10.2174/138920111795163904
  4. Ho-Taek Song, Jin-Suck Suh, Cancer -Targeted MR Molecular Imaging, J Korean Med Assoc, 52, 121-124, 2009 https://doi.org/10.5124/jkma.2009.52.2.121
  5. Gilad AA, McMahon MT, Walczak P, Winnard PT Jr, Raman V, van Laarhoven HW, Skoglund CM, Bulte JW, van Zijl PC, , Artificial reporter gene providing MRI contrast based on proton exchange, Nat Biotechnol., 25, pp.217-219, 2007. https://doi.org/10.1038/nbt1277
  6. A. Yoshitaka, T. Ichkawa, A survey on content-based retrieval for multimedia databases, IEEE Transaction on Knowledge and Data Engineering., Vol. 11, No. 1, 1999..
  7. Jaemoon Yang, Eun-Kyung Lim, Hong Jae Lee, Joseph Park, Sang Cheon Lee, Kwangyeol Lee, Ho-Geun Yoon, Jin-Suck Suh, Yong-Min Huh, Seungjoo Haam, Fluorescent magnetic nanohybrids as multimodal imaging agents for human epithelial cancer detection, Vol. 20, No. 29, pp.2548-2555 2008. https://doi.org/10.1016/j.biomaterials.2007.12.036
  8. Maint BA, Elsen PA and Veirgever MA., 3D multimodality medical image registration using morphological tools, Image Vis. Comput, Vol. 19, pp.53-62, 2001. https://doi.org/10.1016/S0262-8856(00)00051-2
  9. H. Kauppinen, T. Seppanen, M. Pietikainen, An experimental comparison of Autoregressive and Fouier-Based Descriptors in 2D shape Classification, IEEE Transaction on PAMI, Vol. 17, No. 2, pp.201-207, 1995. https://doi.org/10.1109/34.368168
  10. 한동균, 임재동, 이준행, "Wavelet 변환과 경계선 검출 필터를 이용한초음파 영상의 화질증대", 한국방사선학회논문지, Vol. 2, No. 1, pp.23-29, 2008.
  11. 강동길, 박재홍, "웨이블렛 기반의 중복비트제거를 이용한 영상부호화", 한국방사선학회논문지, Vol. 6, No. 1, pp107-111, 2012.
  12. 정화자, DCT를 이용한 윤곽선 추출, 한국정보과학회논문지(C), Vol. 3, No. 1, 1997..
  13. 이상복, 최규락, "분자 MR 영상에서 UTE 신호의 효용성 평가", 한국방사선학회논문지, Vol. 6, No. 4, pp305-311, 2012.
  14. Stephane G. Mallat, A theory for multiresolutional signal decomposition; the wavelet representation, IEEE trans. Pattern Anal. Machine Intell., Vol. 11, No. 7, pp.674-693, 1989. https://doi.org/10.1109/34.192463
  15. Ingrid Daubechies, Ten Lectures on Wavelets, SIAM, 1994.

Cited by

  1. Application and Prospects of Molecular Imaging vol.8, pp.3, 2014, https://doi.org/10.7742/jksr.2014.8.3.123
  2. DWT Analysis of Scatter-Ray Due to the Changed Energy on Digital Medical Images vol.8, pp.2, 2014, https://doi.org/10.7742/jksr.2014.8.1.65