한국방사선학회논문지 (Journal of the Korean Society of Radiology)

  • 제10권8호
  • /
  • Pages.645-652
  • /
  • 2016
  • /
  • 1976-0620(pISSN)
  • /
  • 2384-0633(eISSN)
한국방사선학회 (The Korean Society of Radiology)
권호 표지
DOI QR코드

DOI QR Code

뇌경색 감별진단을 위한 컴퓨터보조진단 응용: Brain CT Images 적용

Computer Aided Diagnosis Applications for the Differential Diagnosis of Infarction: Apply on Brain CT Image

  • 박형후 (한국국제대학교) ;
  • 조문주 (동남권원자력의학원 해부병리과) ;
  • 임인철 (동의대학교 방사선학과) ;
  • 이진수 (인제대학교 해운대백병원 영상의학과)
  • Park, Hyong-Hu (Department of Radiological Science, International University of Korea) ;
  • Cho, Mun-Joo (Department of Pathology, Dongnam Institute of Radiological & Medical Sciences) ;
  • Im, In-Chul (Department of Radiological Science, Dongeui University) ;
  • Lee, Jin-Soo (Department of Radiology, Inje University Haeundae Paik Hospital)
  • 투고 : 2016.12.09
  • 심사 : 2016.12.31
  • 발행 : 2016.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 통계적 속성에 기반한 질감특징값 분석을 바탕으로 뇌 전산화단층촬영 영상에서 정상과 뇌경색의 컴퓨터보조진단의 적용 가능성을 알아보고자 하였다. 실험은 질감특징값을 나타내는 6개의 파라미터를 이용한 질환인식률 평가와 ROC curve를 분석하였다. 그 결과 평균밝기 88%, 대조도 92%, 평탄도 94%, 균일도 88%, 엔트로피 84%의 높은 질환인식률을 나타내었다. 하지만 왜곡도의 경우 58%로 다소 낮은 질환 인식률을 나타내었다. ROC curve를 이용한 분석에서 각 파라미터의 곡선아래면적이 0.886(p=0.0001)이상을 나타내어 질환인식에 의미가 있는 결과로 나타났다. 또한 각 파라미터의 cut-off값 결정으로 컴퓨터보조진단을 통한 질환예측이 가능할 것으로 판단된다.

In this study, based on the analysis of texture feature values of statistical properties. And we examined the normal and the applicability of the computer-aided diagnosis of cerebral infarction in the brain computed tomography images. The experiment was analyzed to evaluate the ROC curve recognition rate of disease using six parameters representing the feature values of the texture. As a result, it showed average mean 88%, variance 92%, relative smoothness 94%, uniformity of 88%, a high disease recognition rate of entropy 84%. However, it showed a slightly lower disease recognition rate and 58% for skewness. In the analysis using ROC curve, the area under the curve for each parameter indicates 0.886 (p = 0.0001) or more, resulted in a meaningful recognition of the disease. Further, to determine the cut-off values for each parameter are determined to be the prediction of disease through the computer-aided diagnosis.

키워드

참고문헌

  1. Jung. Hee. Kim, "Usefulness of Functional MRI for the Study of Brain Function", Korean Brain Society, Vol. 1, No. 1, pp. 65-761, 2001.
  2. Jung. Eun. Byun, Eun. Bum. Kang, "The effects of senior brain health exercise program on basic physical fitness, cognitive function and BDNF of elderly women - a feasibility study", Journal of exercise nutrition & biochemistry, Vol. 20, No. 2, pp. 8-18, 2016. https://doi.org/10.20463/jenb.2016.06.20.2.2
  3. Sung. Hoon. Lee, "Stroke Update 2009: Diagnosis and Stroke Imaging", Korean journal of stroke, Vol. 11, No. 2, pp. 53-55, 2009.
  4. Philip. B. Gorelick, "Primary and Comprehensive Stroke Centers : History Value and Certification Criteria", Korean journal of stroke, Vol. 15, No. 2, pp. 78-89, 2013. https://doi.org/10.5853/jos.2013.15.2.78
  5. Matsumoto, Hiroaki, Yoshida, Yasuhisa, "Rapid Progression of Cerebral Infarction after Intraventricular Hemorrhage in Adult Moyamoya Disease", Journal of Korean Neurosurgical Society, Vol. 54, No. 5, pp. 411-414, 2013. https://doi.org/10.3340/jkns.2013.54.5.411
  6. Young. Dae. Kwon, Sung. Sang. Yoon, Hye. Jung. Chang, "Impact of an Early Hospital Arrival on Treatment Outcomes in Acute Ischemic Stroke Patients", Journal of preventive medicine and public health, Vol. 40, No. 2, pp. 130-136, 2007. https://doi.org/10.3961/jpmph.2007.40.2.130
  7. S. Selvan, S. Ramakrishnan, "SVD-Based Modeling for Image Texture Classification Using Wavelet Trans formation", IEEE transactions on image processing, Vol. 16, No. 11, pp. 2688-2696, 2007. https://doi.org/10.1109/TIP.2007.908082
  8. Montseny. E, Oliver. G, Sobrevilla. P, "Multi-Processor System for Polygonal Object Recognition by Means of Fuzzy Algorithms", International Conference on Pattern Recognition, Vol. 11, No. 4, pp. 170-172, 1992.
  9. Dae. Hun. Kim, Seong. Jin. Ko, Se. Sik. Kang, Jung. Hoon. Kim, Chang. Soo. Kim, "Computer-Aided Diagnosis for Pulmonary Tuberculosis using Texture Features Analysis in Digital Chest Radiography", The Korea Contents Society, Vol. 11, No. 11, pp. 185-193, 2011.
  10. Ji. Hyun. Yoo, Seong. Yong. Ohm, Min. Gyo. Chung, "Maximum-Entropy Image Enhancement Using Brightness Mean and Variance", Korean Society for Internet Information, Vol. 13, No. 3, pp. 61-73, 2012.
  11. Fangyan. Nie, Pingfeng. Zhang, "Image Segmentation Based on Framework of Two-dimensional Histogram and Class Variance Criterion", International Journal of Signal Processing Image Processing and Pattern Recognition, Vol. 8, No. 7, pp. 79-88, 2015.
  12. Suk. Hyung. Hwang, Hae. Sul. Yang, Jung. Ho. Park, "Flattening class hierarchy for reorganization of object-oriented software", Korea Information Processing Society, Vol. 8, No. 6, pp. 853-860, 2001.
  13. Chang. Mo. Yang, Kwang. Sue. Chung, "Embedded Image Compression Scheme Using Rate-Distortion Optimized Block Coding of Wavelet Coefficients", The Journal of the KICS, Vol. 39, No. 11, pp. 625-636, 2014.
  14. IJHIT, "Assessment of Spatial Distribution Uniformity in 2-Dimensional Discrete Space", International Journal of Hybrid Information Technology, Vol. 8, No. 3, pp. 339-346, 2015. https://doi.org/10.14257/ijhit.2015.8.3.30
  15. Kue. Bum. Lee, Dong. Keun. Chung, Kwang. Seok. Hong, "An Implementation of Gaze Direction Recognition System using Difference Image Entropy", The KIPS Transactions : Part B, Vol. 16, No. 2, pp.93-100, 2009.
  16. Yeon. Ok. Park, Eun. Kyung. Jung, Yeon. Jung. Park, So. Ra. Nam, Ji. Young. Jung, Hee. Joung. Kim, "The Evaluation of CR and DDR chest image using ROC analysis", Journal of the Korean Society of Radiology, Vol. 1, No. 1, pp. 25-30, 2007.
  17. Lin. X, Wang. D, "Texture classification using spectral histograms", IEEE Trans Image Process, Vol. 12, No. 6, pp. 661-670, 2003. https://doi.org/10.1109/TIP.2003.812327
  18. Wu. D, Chen. X. J, He. Y, Lin. S, "Study on application of multi-spectral image texture to discriminating rice categories based on wavelet packet and support vector machine", Guang Pu Xue Yu Guang Pu Fen Xi, Vol. 29, pp. 222-225, 2009.