지방간 질환 자동 진단을 위한 3D 간 CT 영상의 분할 및 강체 정합 기법
Segmentation and Rigid Registration Method of 3D Liver CT Images for Automatic Diagnosis of Fatty Liver Disease
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초록
본 논문에서는 지방간 질환의 자동 진단 및 치료에 활용 가능한 비침습적 평가 기준 제공을 위해 동일 환자의 시간차 간 CT 영상의 분할 및 강체 정합 기법을 제안한다. 본 제안 방법은 다음과 같은 네 단계로 구성된다. 첫 번째, 환자의 조영 전 CT 영상에서는 간에 해당하는 영역을, 조영 후 CT 영상에서는 간과 혈관을 분할한다. 두 번째, CPD 알고리즘을 사용하여 조영 전, 후 간 영역 간의 전역적 정렬을 수행한다. 세 번째, 복셀 기반의 정밀 정합을 수행한다. 이 때, 속도 향상을 위해 파웰 최적화 방법을 이용한다. 마지막으로 지방간 측정을 위해 정합이 완료된 간 CT 영상에서 평균 HU 값을 계산한다. 실험 결과, 평균 정합 오차는 4.48mm로 나타났으며 본 제안 방법은 지방간 자동 진단에 유용하게 사용될 수 있음을 확인했다.
Abstract
In this paper, we propose a liver segmentation and rigid registration method to provide non-invasive evaluation criteria that can be used for automatic diagnosis and treatment of fatty liver disease. This proposed method consists of the following four steps. First, we perform liver segmentation with the pre-contrast CT image, and the liver and blood vessels are segmented in the portal phase CT image. Second, by using the CPD algorithm, we perform global alignment between the liver regions of two sequences. Third, we perform voxel-based fine registration. In this case, we use the Powell's method to improve the speed of registration. Finally, for fat volume measurement, we calculate average HU value from the liver CT image. As a result of the experiment, average registration error was 4.48mm and this method can be useful for automatic diagnosis of fatty liver.
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