Journal of the Korean Society of Radiology (한국방사선학회논문지)

The Korean Society of Radiology (한국방사선학회)
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Texture Feature Analysis Using a Brain Hemorrhage Patient CT Images

전산화단층촬영 영상을 이용한 뇌출혈 질감특징분석

  • Park, Hyonghu (Dept. of Radiological Science, International University of Korea) ;
  • Park, Jikoon (Dept. of Radiological Science, International University of Korea) ;
  • Choi, Ilhong (Dept. of Radiological Science, International University of Korea) ;
  • Kang, Sangsik (Dept. of Radiological Science, International University of Korea) ;
  • Noh, Sicheol (Dept. of Radiological Science, International University of Korea) ;
  • Jung, Bongjae (Dept. of Radiological Science, International University of Korea)
  • 박형후 (한국국제대학교 방사선학과) ;
  • 박지군 (한국국제대학교 방사선학과) ;
  • 최일홍 (한국국제대학교 방사선학과) ;
  • 강상식 (한국국제대학교 방사선학과) ;
  • 노시철 (한국국제대학교 방사선학과) ;
  • 정봉재 (한국국제대학교 방사선학과)
  • Received : 2015.08.19
  • Accepted : 2015.10.25
  • Published : 2015.10.30
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Abstract

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

본 연구에서 제안된 질감특징분석 알고리즘은 뇌출혈환자의 CT영상을 이용하여 정상영상과 질환영상으로 구분하여, 고유영상 및 실험영상을 생성하고 제안된 컴퓨터보조진단 시스템에 적용하여 6개의 파라메타로 정량적 분석을 통해 뇌출혈 CT영상의 인식률을 도출하고 평가하였다. 결과로 뇌출혈 CT영상 40증례 중에서 각각의 질감 특징값에 대한 인식률은 평균밝기의 경우 100%, 평균대조도의 경우 100%, 평탄도의 경우 100%, 왜곡도의 경우 100%로 높게 나타났고, 균일도의 경우 95%, 엔트로피의 경우 87.5%로 다소 낮은 질환 인식률을 보였다. 따라서 본 연구의 결과를 바탕으로 의료영상의 컴퓨터보조진단 시스템으로 발전된 프로그램을 구현한다면 뇌출혈 CT영상의 질환부위 자동검출 및 정량적 진단이 가능해 컴퓨터보조진단 자료로서 활용이 가능할 것으로 판단되며 최종판독에서 정확성과 판독시간 단축에 유용하게 사용 될 것으로 사료된다.

Keywords

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