Journal of the Korean Society of Radiology (한국방사선학회논문지)

The Korean Society of Radiology (한국방사선학회)
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Application of Texture Feature Analysis Algorithm used the Statistical Characteristics in the Computed Tomography (CT): A base on the Hepatocellular Carcinoma (HCC)

전산화단층촬영 영상에서 통계적 특징을 이용한 질감특징분석 알고리즘의 적용: 간세포암 중심으로

  • Yoo, Jueun (Department of Radiological Science, Dongeui University) ;
  • Jun, Taesung (Department of Radiological Science, Dongeui University) ;
  • Kwon, Jina (Department of Radiological Science, Dongeui University) ;
  • Jeong, Juyoung (Department of Radiological Science, Dongeui University) ;
  • Im, Inchul (Department of Radiological Science, Dongeui University) ;
  • Lee, Jaeseung (Department of Radiological Science, Dongeui University) ;
  • Park, Hyonghu (Department of Radiology, Bongseng Memorial Hospital) ;
  • Kwak, Byungjoon (Department of Public Health, Daegu Hanny University) ;
  • Yu, Yunsik (Department of Radiological Science, Dongeui University)
  • Received : 2012.10.04
  • Accepted : 2013.02.22
  • Published : 2013.02.28
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Abstract

In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was $40{\times}40$ pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.

본 연구는 전산화단층촬영에서 간 질환의 자동 인식으로 질감특징분석(texture feature analysis. TFA) 알고리즘을 제안하고자 하였으며, 간세포암(Hepatocellular carcinoma. HCC)에 대한 컴퓨터보조진단(computer-aided diagnosis. CAD) 시스템을 설계하고, 제안하는 각 알고리즘의 성능을 평가하고자 하였다. HCC 영상에서 분석영역($40{\times}40$ 픽셀)을 설정하고 각 부분영상에 통계적 특징을 이용한 6가지 TFA 파라메터(평균 밝기, 평균 대조도, 평탄도, 왜곡도, 균일도, 엔트로피)비교하여 간세포암 인식률(recognition rate)을 구하였다. 결과적으로 TFA는 간세포암 인식률을 나타내는 척도로 유의함을 알 수 있었으며 6가지 파라메터에서 균일도가 가장 인식률이 높았으며 평균 대조도, 평탄도, 왜곡도가 비교적 높았고 평균 밝기와 엔트로피는 상대적으로 낮은 인식률을 나타내었다. 이와 관련하여 높은 인식률을 보인 알고리즘(최대 97.14%, 최소 82.86%)을 간세포암 영상의 병변을 판별하여 임상의 조기 진단을 보조하여 치료를 시행한다면 진단의 효율성이 높아 질 것으로 판단되었으며, 향후 효율적이고 정량적인 분석을 추가함으로써 질병인식의 일반화에 대한 기준 연구가 필요 할 것으로 사료되었다.

Keywords

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