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디지털 및 일반 측방두부규격방사선사진에서 측정 방법에 따른 계측치의 비교
김미자,허경회,이원진,허민석,이삼선,이진구,안병근,최순철 대한구강악안면방사선학회 2005 Imaging Science in Dentistry Vol.35 No.1
Purpose : To compare cephalometric measurement between measuring methods in digital and conventional lateral cephalometric radiograph. Materials and Methods : Twenty digital and conventional lateral cephalometric radiographs were selected. In digital group, cephalometric measurements were performed manually using hardcopies and automatically using VCeph^(TM) program on the monitor. In conventional group, the same measurements were performed manually on conventional films, and for automatic measurement conventional films were digitized by scanner. All measurements were performed twice by 4 observers, and 24 cephalometric variables were calculated and the time spent for each measurement was recorded. The differences in measurements data and the time spent for each measurement were compared within each group. Intra-observer and inter-observer comparisons were performed. Results : In both groups, no statistically significant difference between manual and automatic measurements was observed and most of the variables didn’t show statistically significant differences between methods. The observer with less experience tended to show statistically significant differences of measurements between methods, and differences from other observers. The differences of measurements between methods in digital group were lesser than those of conventional group with statistical significance in 8 variables out of 24. With automatic method and in digital group, the spent time was shorter. Conclusion : With direct digital radiograph, automatic method using manually idenitified landmarks can be preferable in cephalometric analysis.
박미라,허명회,정진환 高麗大學校統計硏究所 1990 應用統計 Vol.5 No.1
The aim of this study is to develop more suitable statistical models than the ones that are currently used in two typical cases in food and nutrition research. In the first case which is concerned with ordered categorical responses of a tasting experiment, usual ANOVA model is replaced by a generalized logit model. In the second case which deals with a toxicology experiment, a nonlinear growth curve model is proposed as a substitute of naively conceived linear model.
돼지 하악골의 micro-CT 영상에서 추출한 3차원 골미세구조와 골강도 사이의 상관관계
허경회,박무순,이원진,허민석,이삼선,최순철 대한구강악안면방사선학회 2005 Imaging Science in Dentistry Vol.35 No.3
Purpose : To investigate the relationship between three-dimensional (3D) bone imaging parameters and trabecular strength in the mandible. Materials and Methods : Bone specimens were obtained from the mandibles of five male pigs weighing around 110 kg each. Of those, 43 samples were selected for 3D analysis and measured by micro-computed tomography. The five morphometric parameters were trabecular thickness (Tb.Th), bone specific surface (BS/BV), percent bone volume (BV/TV), structure model index (SMI) and degree of anisotropy (DA). Through destructive mechanical testing, strength parameters were obtained. Results : BV/TV, SMI, BS/BV, and Tb.Th showed significant correlations with strength parameters. DA did not show any correlation with the other parameters. In multiple linear regression analysis, BV/TV alone explained 43% of the variance in Young’s modulus. By stepwise inclusion of SMI, the variance in the Young’s modulus was better explained up to 52%. Conclusions : Predicting trabecular strength in the mandible through architectural analysis would be possible. Further study is needed to establish the tendency and variety of trabecular architecture and strength according to the locations within the mandible.
Fractal analysis of mandibular trabecular bone: optimal tile sizes for the tile counting method
Huh, Kyung-Hoe,Baik, Jee-Seon,Yi, Won-Jin,Heo, Min-Suk,Lee, Sam-Sun,Choi, Soon-Chul,Lee, Sun-Bok,Lee, Seung-Pyo Korean Academy of Oral and Maxillofacial Radiology 2011 Imaging Science in Dentistry Vol.41 No.2
Purpose : This study was performed to determine the optimal tile size for the fractal dimension of the mandibular trabecular bone using a tile counting method. Materials and Methods : Digital intraoral radiographic images were obtained at the mandibular angle, molar, premolar, and incisor regions of 29 human dry mandibles. After preprocessing, the parameters representing morphometric characteristics of the trabecular bone were calculated. The fractal dimensions of the processed images were analyzed in various tile sizes by the tile counting method. Results : The optimal range of tile size was 0.132 mm to 0.396 mm for the fractal dimension using the tile counting method. The sizes were closely related to the morphometric parameters. Conclusion : The fractal dimension of mandibular trabecular bone, as calculated with the tile counting method, can be best characterized with a range of tile sizes from 0.132 to 0.396 mm.
In-vitro study on the accuracy of a simple-design CT-guided stent for dental implants
Huh, Young-June,Choi, Bo-Ram,Huh, Kyung-Hoe,Yi, Won-Jin,Heo, Min-Suk,Lee, Sam-Sun,Choi, Soon-Chul Korean Academy of Oral and Maxillofacial Radiology 2012 Imaging Science in Dentistry Vol.42 No.3
Purpose: An individual surgical stent fabricated from computed tomography (CT) data, called a CT-guided stent, would be useful for accurate installation of implants. The purpose of the present study was to introduce a newly developed CT-guided stent with a simple design and evaluate the accuracy of the stent placement. Materials and Methods: A resin template was fabricated from a hog mandible and a specially designed plastic plate, with 4 metal balls inserted in it for radiographic recognition, was attached to the occlusal surface of the template. With the surgical stent applied, CT images were taken, and virtual implants were placed using software. The spatial positions of the virtually positioned implants were acquired and implant guiding holes were drilled into the surgical stent using a specially designed 5-axis drilling machine. The surgical stent was placed on the mandible and CT images were taken again. The discrepancy between the central axis of the drilled holes on the second CT images and the virtually installed implants on the first CT images was evaluated. Results: The deviation of the entry point and angulation of the central axis in the reference plane were $0.47{\pm}0.27$ mm, $0.57{\pm}0.23$ mm, and $0.64{\pm}0.16^{\circ}$, $0.57{\pm}0.15^{\circ}$, respectively. However, for the two different angulations in each group, the $20^{\circ}$ angulation showed a greater error in the deviation of the entry point than did the $10^{\circ}$ angulation. Conclusion: The CT-guided template proposed in this study was highly accurate. It could replace existing implant guide systems to reduce costs and effort.
허명회,손은진 高麗大學校統計硏究所 2003 應用統計 Vol.18 No.-
Rand index는 군집화의 재현성을 평가하기 위한 자료 분할법에서 두 군집화 결과간의 일치도를 재는 지표이지만 (Rand, 1971) 개체가 1개 군집에 명확히 할당되는 군집화에만 적용될 수 있다. 따라서, 본 연구의 대상인 퍼지 K-평균 군집화(fuzzy K-means clustering)에서는 개체가 각 군집에 속할 소속도(membership)로 제시되므로 Rand index를 원형 그대로 사용할 수 없다. 본 연구의 목적은 퍼지 K-평균 군집화 결과 간 일치성 평가에 활용 가능하도록 Rand index를 확장하는 것이다. 제안 방법을 요약하면 다음과 같다. 1) 훈련 데이터로부터 얻은 퍼지 K-평균 군집화 규칙을 테스트 자료의 각 개체에 적용하여 K개 (=군집 수) 퍼지 소속도를 구한다. 독립적인 다른 훈련 데이터로부터 얻게 되는 퍼지 K-평균 군집화 규칙을 테스트 자료의 동일 개체에 적용하여 또 다른 K개 퍼지 소속도를 구한다. 2) 각 퍼지 군집화 규칙에 따른 군집 소속도에 비례하게 테스트 자료의 개체를 독립적으로 K개 군집 중 하나에 임의 할당하는 역 퍼지화 작업을 시행하여 명확한 분할(hard partition) 자료를 만든다. 3) 대응하는 두 개의 분할 군집화 결과로부터 통상적인 Rand index (또는 Hubert and Arabie (1985)의 C.(corrected) Rand index)를 산출한다. 4) 앞의 두 단계를 일정 수 반복하여 Rand index의 몬테칼로(Monte Carlo) 분포를 산출한다. 그 분포의 평균을 확장(extended) Rand index로 정의한다. 퍼지 K-평균 군집화에서 군집 수 K를 결정하는 문제에 확장 Rand index를 활용할 수 있다. 몇 개의 적용 사례를 제시하고 토의할 것이다. Rand index is an evaluation measure of consistency between two clustering rules (Rand. 1971). Hence it can be used to predict whether the clustering patterns are reproducible in the future. The index, however, cannot be applied to the fuzzy K-means clustering which has clear merits in dealing with overlapping clusters. The aim of this study is to extend Rand index or corrected Rand index of Hubert and Arabie (1985) for the use in fuzzy K-means clustering. The proposed method can be summarized as follows : Step 1: Partition the data into three parts - two training samples and one. test sample. Then, derive a K-means clustering rule from the first training sample and another rule from the second training sample. Then, apply both rules separately to the test sample units to obtain the list of cluster membership pairs. Step 2: Perform the inverse procedure opposite to make things fuzzy. In other words, generate a pair of hard partitions according to respective memberships of fuzzy partitions. Step 3: Compute Rand index or corrected Rand index of Hubert and Arabie (1985) from a pair of hard partitions. Step 4: Repeat Steps 3 and 4 for sufficient number of times. Then, one obtains a batch of Rand indices. Define Extended Rand Index by the average of Rand indices. We may use Extended Rand Index in determination of the number of clusters Kin fuzzy K-means clustering. Several examples are illustrated.