무릎 자기공명영상에서 형상 사전정보 기반의 그래프 컷을 이용한 전방십자인대 분할 = Anterior Cruciate Ligament Segmentation from Knee MR Images Using Graph Cuts with Shape Priors

국문 초록 (Abstract)

무릎 자기공명영상에서 전방십자인대는 연골 및 후방십자인대와 같은 주변 연부조직들과 유사한 밝기값을 가지며 인접해 있어 기존의 그래프 컷과 같은 밝기값 기반의 분할을 수행할 경우 주변조직으로의 누출이 나타난다. 본 논문에서는 이러한 문제를 해결하기 위해 무릎 자기공명영상에서 형상 사전정보기반의 그래프 컷을 이용한 전방십자인대 분할기법을 제안한다. 제안방법은 두 단계로 구성된다. 첫째, 가우시안 혼합 모델 기반의 적응적 임계화와 형태학적 연산을 이용해 그래프 컷의 씨앗 정보를 추출한다. 둘째, 추출한 씨앗 정보의 형상 사전정보를 이용하여 그래프 컷을 수행, 전방십자인대 영역을 분할한다. 제안방법의 성능 평가를 위해 육안평가 및 정확성 평가를 수행하였으며, 실험결과 기존의 그래프 컷과 비교, 주변 조직으로의 누출 없이 전방십자인대의 분할 정확도가 향상된 것으로 나타났다.

다국어 초록 (Multilingual Abstract)

In this paper, we propose an anterior cruciate ligament (ACL) segmentation method in knee MR images using graph cuts with intensity and shape priors. Our method consists of two steps. First, object and background seeds for graph cuts are extracted using adaptive thresholding based on Gaussian mixture model and morphological operation on coronal and sagittal planes. Second, graph cuts are performed to segment ACL with intensity and shape priors information of extracted object and background seeds. In knee MR images, since ACL shares similar intensity with near soft tissues and some of these tissues e.g. posterior cruciate ligament (PCL) are even adjacent to ACL, leakage to these tissues occurs when an intensity-based segmentation is performed. To solve this problem, we propose the technique of representing shape priors from extracted object and background seeds, not from segmented images, and reflecting these shape priors to the graph cuts. To evaluate the performance of our method, visual inspection and accuracy evaluation were performed. Compared to the results of original graph cuts, experimental results of our method show improved segmentation accuracy without leakage into neighboring soft tissues by applying shape priors to the graph cuts.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 형상 사전정보 기반의 그래프 컷을 이용한 전방십자인대 분할
• 3. 실험 및 결과
• 4. 결론
• References

참고문헌 (Reference)

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