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Evaluation and Comparison of Signal to Noise Ratio According to Change of Kernel size of Heart Shadow on Chest Image

흉부 영상에서 커넬 크기변화에 따르는 신호대잡음비 비교평가

  • Lee, Eul-Kyu (Department of Radiology, Inje Paik University Hospital at Jeo-dong) ;
  • Jeong, Hoi-Woun (Department of Radiological Technology, The Baekseok Culture University) ;
  • Min, Jung-Whan (Department of Radiological Technology, The Shingu University)
  • 이을규 (인제대학교 서울백병원 영상의학과) ;
  • 정회원 (백석문화대학교 방사선과) ;
  • 민정환 (신구대학교 방사선과)
  • Received : 2017.09.15
  • Accepted : 2017.11.30
  • Published : 2017.11.30

Abstract

The purpose of this study was to comparison of measure signal to noise ratio (SNR) according to change of kernel size from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 100 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p<0.05). In SNR results, with the quality of distributions in the order of kernel size 9*9 image, kernel size 7*7 image and original chest image, kernel size 3*3 image (p<0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the kernel size chest image.

본 연구는 흉부영상에서 심장저부 관심영역(region of interest; ROI)의 신호 대 잡음비(signal to noise ratio; SNR)를 커넬 크기 변화에 따르는 비교 평가하였다. 연구대상은 종합대학병원에서 흉부 검사한 환자 100명을 대상으로 하였다. 측정은 ImageJ 프로그램을 사용하여 표본의 사회학적 특성및 흉부영상들의 SNR평균 값, SNR평균차이, 95% 신뢰구간 값, 등을 분석하였다. 이때 SPSS Statistics21 통계프로그램으로 ANOVA 분석을 하였으며, 95%(p<0.05)에서 유의한 것으로 판단하였다. 흉부영상을 분석결과는 SNR이 kernel size 9*9 image, kernel size 7*7 Image, original chest image, kernel size 3*3 image순으로 높은 값으로 나타냈다(p<0.001). 결론적으로, 본 연구에서는 흉부 의료영상에서 커넬 크기 변화에 따라서 심장저부 음영의 정량화한 평가 결과를 방사선사의 보조적인 수단으로 활용할 수 있을 것으로 사료된다.

Keywords

References

  1. Min, J. W., Kim, J. M., Jeong, H. W., et al., "Research about filter association and clinical effect noise reduction of digital medical imaging system", Journal of Radiological Science and Technology, Vol. 30, No. 4, pp. 329-334, 2007.
  2. Min, J. W., Kim, J. M., Jeong, H. W., et al., "Artifacts in Digital Radiography", Journal of Radiological Science and Technology, Vol. 38, No. 4, pp. 375-381, 2015. https://doi.org/10.17946/JRST.2015.38.4.06
  3. Kim, K. W., Lee, E. K., Min, J. W., et al., "Evaluation and Comparison of Signal to Noise Ratio According to Histogram Equalization of Heart Shadow on Chest Image", Journal of Radiological Science and Technology, Vol. 40, No. 2, pp. 197-203, 2017. https://doi.org/10.17946/JRST.2017.40.2.03
  4. Kenichi F., Ruriko Y., Mitsuei S., et al., "Measurement of Gradation Curve by the Digital Test Pattern Method in a Computed Radiography System", Japanese Society of Radiological Technlogy, Vol. 60, No. 7, pp. 1000-1008, 2004. https://doi.org/10.6009/jjrt.KJ00000922536
  5. Doi K., "Diagnostic imaging over the last 50 years: research and development in medical imaging science and tecnology", Phys Med Biol, Vol. 51, No. 13, pp. R5-R27, 2006. https://doi.org/10.1088/0031-9155/51/13/R02
  6. P. Shanmugavadivu., K. Balasubramanian., "Image Edge and Contrast Enhancement Using Unsharp Masking and Constrained Histogram Equalization", Communications in Computer and Information Science, Vol. 140, No. 1, pp. 129-136, 2011.
  7. REGIUS 150 Technology Curriculum Guide, KONICA, Ver. 1.0 2001.
  8. REGIUS model 150 Imaging formation Theory, KONICA, 2001.
  9. Kim, E. K., "Contrast and geometric correction of non-standardized radiographs in digital subtraction radiography", The journal of Korean academy of periodontology, Vol. 28, No. 4, pp. 797-809, 1998. https://doi.org/10.5051/jkape.1998.28.4.797
  10. Min, J. W., Jeong, H. W., Kim, K. W., et al., "Comparison Study on CNR and SNR of Thoracic Spine Lateral Radiography", Journal of Radiological Science and Technology, Vol. 36, No. 4, pp. 280-273, 2013.
  11. Lee, E. K., Kim, K. W., Min, J. W., et al., "Statistical Approach of Measurement of Signal to Noise Ratio in According to Change Pulse Sequence on Brain MRI Meningioma and Cyst Images", Journal of Radiological Science and Technology, Vol. 39, No. 3, pp. 345-352, 2016. https://doi.org/10.17946/JRST.2016.39.3.07
  12. Lee, J. Y., Lee, E. K., Min, J. W., et al., "Evaluation and Comparison of Contrast to Noise Ratio and Signal to Noise Ratio According to Change of Reconstruction on Breast PET/CT", Journal of Radiological Science and Technology, Vol. 40, No. 1, pp. 79-85, 2017. https://doi.org/10.17946/JRST.2017.40.1.12
  13. Schaetzing R., "Computed radiography technology", Proceeding of Radiological Society of North America, 10, 2003.
  14. KFDA., "A guide for general radiology of the patient dose recommend", 2012.
  15. Jerrold T.B., Anthony S.J., Edwin M. L., et al., "The Essential Physics of Medical Imaging, 3nd ed.", Lippincott Williams & Wilkins, 2011.