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Breast PET CT 영상 재구성 변화에 따른 대조도 대 잡음비와 신호 대 잡음비의 비교평가
이재영(JeaYoung Lee),이을규(EuKyu Lee),김기원(KiWon Kim),정회원(HoiWoun Jeong),유광열(KwangYeul Lyu),박훈희(HoonHee Park),손진현(JinHyun Son),민정환(JungWhan Min) 대한방사선과학회(구 대한방사선기술학회) 2017 방사선기술과학 Vol.40 No.1
본 연구는 양전자 컴퓨터단층촬영(Positron Emission Computed Tomography; PET-CT) 유방암 영상에서 질환관 심영역(Region of Interest; ROI)의 대조도 대 잡음비(Contrast to Noise Ratio; CNR)와 신호 대 잡음비(Signal to Noise Ratio; SNR)를 재구성 기법별로 측정 평가하여 통계적 분석을 하고자 하였다. 연구대상은 서울소재 대학병원에서 유방 양전자 컴퓨터단층촬영(Breast PET-CT)을 검사한 환자 100명이다. 측정방법은 ImageJ 프로그램을 사 용하여 표본의 인구사회학적 특성, 영상 재구성 기법별 유방암의 SNR과 CNR의 평균값, 95% 신뢰구간 값, SNR과 CNR의 평균차이 값 등을 분석하였다. SPSS Statistics21 통계프로그램으로 ANOVA 분석을 하였으며 p<0.05에서 유의한 것으로 판단하였다. 영상 재구성 기법별 분석결과는 CNR과 SNR 모두 TrueX_TOF, Iterative, Iterative-TOF, FBP-TOF 순으로 높은 값을 나타냈다(p<0.05). CNR과 SNR수치로 영상 재구성 기법의 특징을 조합하여 PET/CT 검사의 정확성과 편의성을 제공할 것이라 사료된다. The purpose of this study was to measure contrast to noise ratio (CNR) and signal to noise ratio (SNR) according to change of reconstruction from region of interest (ROI) in breast positron emission tomography- computed tomography (PET-CT), and to analyze the CNR and SNR statically. We examined images of breast PET-CT of 100 patients in a University-affiliated hospital, Seoul, Korea. Each patient’s image of breast PET-CT were calculated by using ImageJ. Differences of CNR and SNR among four reconstruction algorithms were tested by SPSS Statistics21 ANOVA test for there was statistical significance (p<0.05). We have analysis socio-demographical variables, CNR and SNR according to reconstruction images, 95% confidence according to CNR and SNR of reconstruction and difference in a mean of CNR and SNR. SNR results, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR and SNR of PET-CT reconstruction methods of the breast would be useful to evaluate breast diseases.
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동적문제출제시스템에서 NLPMS를 위한 오답 계명 생성
김명신(MyungShin Kim),김태홍(TaeHong Kim),이재영(JeaYoung Lee) 한국정보기술학회 2007 Proceedings of KIIT Conference Vol.2007 No.-
음악의 본질과 아동의 특성을 고려하여 복잡한 지적사고 과정없이 시범, 모방과 음악적 활동을 통해서 자연스럽게 음악을 배울 수 있는 자연적 음악교육 방법이 NLPMS에서 노래, 신체동작, 색, 도형 등을 사운드, 동영상, 그래픽 등의 멀티미디어로 학생을 평가하는 시스템에서 답이 계명으로만 이루어지는 특정을 이용하여 출제자가 만들어야 하는 오답 계명을 난수와 음의 대응 방식으로 자동 생성하는 방법에 대하여 연구하였다. NLPMS is the natural music learning process that students are able to learn music through demonstration, imitation and musical activities without complex intellectual thinking considering nature of music and characteristics of children. We researched the formation of incorrect sol-fa answers automatically in order to decrease examination maker's burdens using by the characteristics which answers is only represented by sol-fa names in the evaluation system that students are able to learn music using multimedia such as sounds, animations, images, and graphics instead of musical activities in class such as songs, body movements, colors, and figures in NLPMS.
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김명신(MyungShin Kim),김태홍(TaeHong Kim),이재영(JeaYoung Lee) 한국정보기술학회 2006 한국정보기술학회논문지 Vol.4 No.2
NLPMS is the natural music learning process that students are able to learn music through demonstration, imitation and musical activities without complex intellectual thinking considering nature of music and characteristics of children, in order to improve the traditional music learning methodology based on cognitions and scientific principles. We researched the learning system that students are able to learn music using multimedia such as sounds, animations, images, and graphics in distance learning instead of musical activities in class such as songs, body movements, colors, and figures in NLPMS. In additions, for many different kinds of tests we proposed the system to generate dynamic test that relocate questions and items selected from question information database by random numbers, whenever students try to test.
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오정석(JungSeok Oh),조우진(WooJin Cho),김유섭(YuSeop Kim),이재영(JeaYoung Lee) 한국정보기술학회 2005 한국정보기술학회논문지 Vol.3 No.4
In this paper, we utilized information retrieval methodologies for prompt and impartial marking. We constructed semantic kernels from untagged corpus, vectorized examination papers composed bylearners and examiners, computed similarity of two examination papers through semantic kernels, and decided whether or not the paper is correct. For semantic kernels, we used methodologies based on two vector space models, one is generalized vector space model (GVSM) and the other is latent semantic analysis (LSA). In this paper, we mark the real examination papers by using automatic marking systems based on semantic kernels, and we can acquire about 80% accuracy compared to a manual marking.
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