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흰쥐의 체내 무기질 및 간장 조직 변화에 미치는 계피첨가 식이의 영향
조수열,박호병,정재홍 嶺南大學校 環境問題硏究所 1986 環境硏究 Vol.6 No.1
This study was undertaken to observe the effect of cinnamon diet on mineral status and liver histologic change in rats. Twenty eight young male albino rats, Sparague-Dawley strain, body weight of 100 ±10g were employed in the study. They had been fed for 7 weeks. a control diet (CS) and experimental diets containing 0.2% cinnamon powder(CA), 1.0% cinnamon powder(CB) and 5.0% cinnamon powder(CC). The contents of serum calcium, magnesium, sodium and potassium showed increasing tendency in the groups fed cinnamon diets. Serum copper was decreased significantly, whereas serium iron was increased in the group containg 1.0% cinnamon powder compared to the control group. The contents of mineral in liver was not changed in all groups, significantly, but zinc was decreased in the group containing 5% cinnamon powder. The parenchymal cell of liver was exhibited mild vacuolar degeneration according to cinnamon addition level.
Evaluation of low-dose lung computed tomography (CT) using deep-learning: A phantom study
Daehong Kim,Kihong Son,Sooyeul Lee,Cheol-Ha Baek,Pil-Hyun Jeon 한국자기학회 2021 한국자기학회 학술연구발표회 논문개요집 Vol.31 No.2
The aim of this phantom study was to evaluate the image quality of low-dose lung computed tomography (CT) achieved using a deep-learning based image reconstruction method. The chest phantom was scanned with a tube voltage of 100 kV for various CT dose index (CTDIvol) conditions: 0.4 mGy for ultra-low-dose (ULD), 0.6 mGy for low-dose (LD), 2.7 mGy for standard (SD), and 7.1 mGy for large size (LS). The signal-to-noise ratio (SNR) and noise values in reconstructions produced via filtered back projection (FBP), iterative reconstruction (IR), and deep convolutional neural network (DCNN) were computed for comparison. The quantitative results of both the SNR and noise indicate that the adoption of the DCNN makes the image reconstruction in the ULD setting more stable and robust, achieving a higher image quality when compared with the FBP algorithm in the SD condition. Compared with the conventional FBP and IR, the proposed deep learning-based image reconstruction approach can improve the ULD CT image quality while significantly reducing the patient dose. 〈그림 본문참조〉
정지욱(Ji-Wook Jeong),이수열(Sooyeul Lee),김승환(Seunghwan Kim) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.2Ⅱ
본 연구에서는 초음파 영상에서 간실질의 에코 명도를 비롯한 픽셀 정보분포를 분석하여 정량화 지방간 진단 파라미터를 구하기 위해 규준화 에코 명도값 및 다수의 텍스쳐 파라미터 값을 추출하여 선형결합을 통해 지방간의 진행 정도와의 상관성을 연구하였다. 임상 지방간지수와 본 연구의 추정 지방간 지표 값과의 선형 상관 계수를 구하였다. 신장대조 방법으로 추출한 규준화 에코 명도 및 회색도 픽셀분포의 텍스쳐 특성 파라미터를 계산하여 임상결과와 비교한 결과, 임상 지방간지수와 높은 상관성을 보임을 알 수 있었고, 지방간 진단의 보조자료로 유용함을 확인하였다. 계산된 지방간지수와 임상결과 간의 선형상관계수는 0.84~0.93이다.
Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images
Jeong, Ji-Wook,Yu, Donghoon,Lee, Sooyeul,Chang, Jung Min Korean Society of Medical Informatics 2016 Healthcare Informatics Research Vol.22 No.4
<P><B>Objectives</B></P><P>We propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique.</P><P><B>Methods</B></P><P>One hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates.</P><P><B>Results</B></P><P>An automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62.</P><P><B>Conclusions</B></P><P>The breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.</P>
정지욱(Ji-Wook Jeong),이수열(Sooyeul Lee),김승환(Seunghwan Kim),조준식(June-Sik Cho) 한국정보과학회 2001 한국정보과학회 학술발표논문집 Vol.28 No.2Ⅱ
본 연구에서는 임상적으로 얻어진 95개의 영상 자료를 전산화하여 특정 간 영역의 명도분포를 분석하여 이와 간의 지방화 정도와의 상관성을 연구하였다. 지방화 정도를 판단하는 임상적 기준으로 보편적으로 인정되는 지방간지수와 계산된 평균 명도 수치와의 선형 상관 계수를 구하였다. 각각의 영상의 밝기 및 에코정도가 일반적으로 불균일하기 때문에 이를 보정하기 위해 밝은 명도와 어두운 명도의 기준영역을 선정하여 상대명도를 추출하였다. 두가지 독립적인 방법으로 기준 영역을 선택하여 비교한 결과, 임상 지방간지수와 높은 상관성을 보임을 알 수 있었고, 지방간 진단의 보조자료로 유용함을 확인하였다. 계산된 지방간지수와 상대명도의 상관계수는 0.69에서 0.79로 나타났다.