• Korean Journal of Acupuncture
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• v.31 no.2
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• pp.66-78
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• 2014

Objectives : The specificity of acupuncture point has been a highly controversial subject. Existing researches said that ion-distribution differences are observed on the acupuncture point. This study was conducted under the assumption that multiple ionic changes induced by muscle fatigue would be different between the acupuncture point with non-acupuncture point. Methods : To induce the identical fatigue, twenty subjects performed the knee extension/flexion exercise using the Biodex System 3. ST32 and ST33 as well as adjacent non-acupuncture points were selected. We measured blood lactate and analyzed the median frequency(MF) and peak torque. To obtain the information on the extracellular fluid(ECW), intracellular fluid(ICW) and cell membrane indirectly, we used the multi-frequency bioelectrical impedance analysis(MF-BIA) method. Results : MF, peak torque and blood lactate level of all measurement sites were gradually returned to normal. Re resistance of ST32 had a stronger response, but a non-acupuncture point adjacent to ST33 had a larger response up to 20 minutes post exercise. Ri resistances were similar for both acupoints and non-acupoints. The $C_m$ capacitance of ST32 had a stronger response after inducing fatigue, but ST33 had a smaller response than a non-acupuncture point adjacent to it. Conclusions : In comparison with before and after inducing fatigue, the specificity of acupuncture points was not clearly observed. Hence, we concluded that the body composition factors extraction method had the limitation as a method of finding the specificity of acupuncture points by inducing fatigue.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.