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무릎 MR 영상에서 다중 아틀라스 기반 지역적 가중투표를 이용한 대퇴부 연골 자동 분할
김현아(Hyeun A Kim),김현진(Hyeonjin Kim),이한상(Han Sang Lee),홍헬렌(Helen Hong) 한국정보과학회 2016 정보과학회논문지 Vol.43 No.8
본 논문에서는 무릎 MR 영상에서 다중 아틀라스 기반 지역적 가중투표를 이용한 대퇴부 연골자동 분할 방법을 제안한다. 제안하는 방법은 다음의 두 단계로 구성된다. 첫째, 대퇴부 연골이 대퇴골에 붙어 있다는 형상정보를 이용하기 위해 볼륨 및 객체 정합 기반의 지역적 가중투표와 협대역 영역확장을 통해 대퇴골을 분할한다. 둘째, 대퇴골의 객체 기반 어파인 변환을 대퇴부 연골 정합에 적용한 후, 다중 아틀라스 형상 기반의 지역적 가중투표를 통해 대퇴부 연골을 분할한다. 제안 방법의 성능을 평가하기 위해 다수투표 기법, 밝기값 기반 지역적 가중투표 기법과 제안 방법의 분할 결과를 전문가에 의한 수동 분할 결과와 비교한다. 실험 결과 제안 방법이 주변 유사 밝기값 영역으로의 누출을 방지하여 분할 정확도가 향상되었음을 보여준다. In this paper, we propose an automated segmentation method of femoral cartilage in knee MR images using multi-atlas-based locally-weighted voting. The proposed method involves two steps. First, to utilize the shape information to show that the femoral cartilage is attached to a femur, the femur is segmented via volume and object-based locally-weighted voting and narrow-band region growing. Second, the object-based affine transformation of the femur is applied to the registration of femoral cartilage, and the femoral cartilage is segmented via multi-atlas shape-based locally-weighted voting. To evaluate the performance of the proposed method, we compared the segmentation results of majority voting method, intensity-based locally-weighted voting method, and the proposed method with manual segmentation results defined by expert. In our experimental results, the newly proposed method avoids a leakage into the neighboring regions having similar intensity of femoral cartilage, and shows improved segmentation accuracy.
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원격교육 성인학습자의 학업성취도와 학업지속 행동에 영향을 미치는 요인에 대한 연구
김현진(Kim Hyunjin),김현아(Kim Hyun-Ah) 한국방송통신대학교 미래원격교육연구원 2011 평생학습사회 Vol.7 No.3
The purpose of this study was to investigate which factors predict undergraduates’ academic achievement and their academic persistence behavior in cyber university. As predicting variables, demographic information, prior learning preparation variables(experience of cyber learning, their perceived level of achievement in the final school), learner’s individual characteristics variables including motivation, attribution, self-efficacy, and self-regulated learning strategies, and learning process variables involving academic development, social integration, and extra-institutional integration were selected. Students were asked to responded a set of surveys including predicting variables in the middle of semester. Academic achievement was based on their total GPA obtained in that semester and academic persistence behavior referred to whether they registered or not in the following semester. This study sampled a total of 487 first year and transfer students, 397(81.5%) persisters, 90(18.5%) wiredrawers, who first enrolled in C cyber university in 2008. Data were first analyzed with t-test, ANOVA, χ2 test, multiple regression and binary logistic regression. The following results were obtained from the analysis. First, among the related variables selected in this study, year, reason for entrance as degree acquisition and self-efficacy, and the use of self-regulated learning strategies were found to show statistically significant in predicting their academic achievement. Especially, entrance motive for degree acquisition was negatively associated with their academic achievement. Second, academic persistence was significantly predicted by goal commitment as students’ motivation, active interaction with instructor as an attribution, and academic development and extra-institutional integration of learning process variables. Finally, based on the results of the study, practical implication for lifelong education in distance education institute such as cyber university, limitations, and suggestions for future study are being discussed.
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