무릎 자기공명영상에서 형상 사전정보 기반의 그래프 컷을 이용한 전방십자인대 분할

이한상(Hansang Lee),홍헬렌(Helen Hong),김준모(Junmo Kim) 한국정보과학회 2014 정보과학회논문지 : 소프트웨어 및 응용 Vol.41 No.1

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. 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.

Noninformative priors for the common shape parameter of several inverse Gaussian distributions

Sang Gil Kang,Dal Ho Kim,Woo Dong Lee 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.1

In this paper, we develop the noninformative priors for the common shape parameter of several inverse Gaussian distributions. Specially, we want to develop noninformative priors which satisfy certain objective criterion. The probability matching priors and reference priors of the common shape parameter will be developed. It turns out that the second order matching prior does not exist. The reference priors satisfy the first order matching criterion, but Jeffrey's prior is not the first order matching prior. We showed that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.

Noninformative priors for the common shape parameter of several inverse Gaussian distributions

Kang, Sang Gil,Kim, Dal Ho,Lee, Woo Dong The Korean Data and Information Science Society 2015 한국데이터정보과학회지 Vol.26 No.1

In this paper, we develop the noninformative priors for the common shape parameter of several inverse Gaussian distributions. Specially, we want to develop noninformative priors which satisfy certain objective criterion. The probability matching priors and reference priors of the common shape parameter will be developed. It turns out that the second order matching prior does not exist. The reference priors satisfy the first order matching criterion, but Jeffrey's prior is not the first order matching prior. We showed that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.

Noninformative priors for the common shape parameter of several inverse Gaussian distributions

강상길,김달호,이우동 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.1

In this paper, we develop the noninformative priors for the common shape parameterof several inverse Gaussian distributions. Specially, we want to develop noninformativepriors which satisfy certain objective criterion. The probability matching priors andreference priors of the common shape parameter will be developed. It turns out thatthe second order matching prior does not exist. The reference priors satisfy the rstorder matching criterion, but Jerey's prior is not the rst order matching prior. Weshowed that the proposed reference prior matches the target coverage probabilities in afrequentist sense through simulation study, and an example based on real data is given.

Bayesian testing for the homogeneity of the shape parameters of several inverse Gaussian distributions

이우동,김달호,강상길 한국데이터정보과학회 2016 한국데이터정보과학회지 Vol.27 No.3

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jeffreys' prior or the reference prior can be used. Jeffreys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

Bayesian testing for the homogeneity of the shape parameters of several inverse Gaussian distributions

Woo Dong Lee,Dal Ho Kim,Sang Gil Kang 한국데이터정보과학회 2016 한국데이터정보과학회지 Vol.27 No.3

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jeffreys` prior or the reference prior can be used. Jeffreys` prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

Bayesian Hypothesis Testing for Homogeneity of the Shape Parameters in the Gamma Populations

강상길,김달호,이우동 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.4

In this paper, we consider the hypothesis testing for the homogeneity of the shape parameters in the gamma distributions. The noninformative priors such as Jeffreys' prior or reference prior are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian testing procedure for the homogeneity of the shape parameters based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

사전 형상 정보를 반영한 Chan-Vese 모델 기반의 자동 윤곽화

강혜원(Hyewon Kang),홍헬렌(Helen Hong) 한국정보과학회 2013 정보과학회논문지 : 소프트웨어 및 응용 Vol.40 No.6

본 논문은 입력받은 두 개의 윤곽선을 이용하여 중간 슬라이스의 윤곽선을 자동으로 추출하는 방법을 제안한다. 첫째, 형상기반 보간을 통해 형상 차이가 큰 경우에도 객체 형상에 근사한 초기 윤곽선을 생성함으로써 분할의 정확성을 높인다. 둘째, 유사한 밝기값을 가진 주변 영역으로의 누출을 제한하기 위해 초기 윤곽선을 기준으로 협대역을 설정하고, 협대역내 남아있는 누출을 제거하고 사용자 입력과 유사한 윤곽선을 추출하기 위해 사전 형상 정보를 반영한 Chan-Vese 모델을 이용하여 객체 영역을 추출한다. 셋째, 형태학적 연산 및 경계 추적 기법을 통해 최종 윤곽선을 추출한다. 제안방법의 성능 평가를 위해 복부 CT 영상과 동적 심장 CT 영상을 이용하여 육안평가, 정확성평가와 수행시간을 측정한다. 육안평가는 복부 CT 영상에서 주변에 유사한 밝기값을 갖는 간의 윤곽선을 견고하게 윤곽화함을 확인하였고, 동적심장 CT 영상에서 객체의 경계가 불연속적이고 곡률이 큰 좌심실 내벽을 사용자가 입력한 윤곽선과 유사하게 윤곽화함을 확인하였다. 정확성 평가는 제안된 방법으로 추출된 윤곽선 결과와 임상전문의에 의해 수동으로 추출된 윤곽선 결과 간의 평균 대칭 거리와 중복 영역 비율로 평가한다. 동적 심장 CT 영상과 복부 CT 영상에 적용한 결과 평균 중복영역비율은 95% 이상으로 측정되었고, 평균 거리의 차는 0.67±0.15 mm 로 1~2개 화소보다 작게 측정되었다. 제안방법을 적용하여 윤곽선을 추출하는데 한 슬라이스 당 평균 0.22초의 수행시간이 소요되었다. 본 제안방법은 의료 진단영상을 이용한 방사선 치료 계획 시 장기의 윤곽선을 추출 할 때 활용될 수 있다. In this paper, We propose a method for automatic contouring of middle slices similar to input contours using the improved Chan-Vese model with shape prior. First, to enhance the accuracy of the next steps when the object has a large shape differences, initial contours of middle slices are generated using shape-based interpolation. Second, to reduce the leakage, narrowbands are defined using the initial contours. And in order to extract the object similar to user input and get rid of leakakge, Chan-Vese model is combined with shape prior. The object is extracted from image using the improved Chan-Vese model. Finally, to extract the contours of middle slices from result of previous step, morphological operator and chain code are performed. Our method has been applied to ten patients in abdominal contrast-enhanced CT imaging and dynamic cardiac CT imaging. For the evaluation of our method, we performed the visual inspection, accuracy measures and processing time. For visual inspection, in spite of object having similar intensity around such as kidneys, ribs and so on, contours of liver were extracted robustly. Contours of left ventricular endocardial that the boundary had a large curvature and discontinuity were extracted similar to input contours. The accuracy was measured using average symmetric distance and volume overlapping rate between automatic segmentation and manual segmentation by a radiologists. The average symmetric distance was 0.67±0.15 mm and overlapping rate was over 95%. The average processing time of contouring was 0.22 seconds per slice. Our method can be used planning of imaging guided radiation therapy.

Bayesian testing for the homogeneity of the shape parameters of several inverse Gaussian distributions

Lee, Woo Dong,Kim, Dal Ho,Kang, Sang Gil The Korean Data and Information Science Society 2016 한국데이터정보과학회지 Vol.27 No.3

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jereys' prior or the reference prior can be used. Jereys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

Noninformative priors for the shape parameter in the generalized Pareto distribution

Kang, Sang Gil,Kim, Dal Ho,Lee, Woo Dong The Korean Data and Information Science Society 2013 한국데이터정보과학회지 Vol.24 No.1

In this paper, we develop noninformative priors for the generalized Pareto distribution when the parameter of interest is the shape parameter. We developed the first order and the second order matching priors.We revealed that the second order matching prior does not exist. It turns out that the reference prior satisfies a first order matching criterion, but Jeffrey's prior is not a first order matching prior. Some simulation study is performed and a real example is given.