• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• Annual Conference of KIPS
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• 2021.11a
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• pp.779-782
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• 2021

Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.

• Journal of Chest Surgery
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• v.28 no.5
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• pp.443-446
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• 1995

The present study was designed to determine whether cyclosporine A inhibits Major Histocompatibility Complex Class II antigen[MHC II expression in the allograft rat heart myocardium. In this rat allograft study we also tried to elucidate whether CSA inhibits MHC II in a dose dependent way. Hearts were isolated from LBN rats weighing 200-250 grams and heterotopically transplantated into the abdomen of weight-matched ACI rats. The ACI rats were randomly assigned to one of the three experimental groups according to cyclosporine dosage: {1}control [no CSA , n=6 {2}CSA 5 mg/day , n=5 {3}CSA 10 mg/day, n=5. The transplanted hearts were harvested 5 days postoperatively and analyzed. MHC II expression was detected by indirect immunoperoxidase staining and quantified via computer image analysis system. The % positive area reading was obtained in each slide [50 areas per group and compared to other groups. Significant differences were noted between three groups [p<0.05 . We conclude that CSA inhibits MHC II in heterotopically transplanted allograft rat heart in a dose dependent way.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.283-284
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• 2005

Al electrode for OLED was deposited by Facing Targets Sputtering(FTS) system which can reduce the damage of organic layer. The Al thin films were deposited on the slide glass as a function of working gas such as Ar, Kr or mixed gas. The film surface image was observed by AFM and SEM. In the results, when Al thin film were deposited using mixed gas, the surface morphology was improved in some region.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1074-1077
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• 2002

Biomechanical behavior of the human femur is very important in various clinical situations. In this study, the data of FE models based on DICOM file exported from Computed tomography(CT). We generated FE models(voxel model, tetra model) of human femur using CT slide image. We compared them with Yon Mises stress results derived from finite element analysis(FEA). Comparing the two models, we found a correlation of them. As a result, the tetra model based proposed marching cube algorithm is a valid and accurate method to predict parameters of the complex biomechanical behavior of human femur.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.1
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• pp.18-35
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• 1997

This research aims at exploring the relationship between urban greenery and urban landscape and thereby deriving proper planning indicators of urban landscape, For the purpose, this research used survey research method which asked urban residents to answer the research questionnaires about the urban landscape which was presented by slide projection. The analysis results are as follows: 1) urban residents have highly positive image about the greenery; 2) they highly evaluate urban beautification and amenity function of the greenery; 3) the supply of the greenery is very short regardless of the survey locations; 4) due to the small amount of the greenery, its role in landscape is little. Landscape assessment can be approached from various aspects. Urban residents cognitively regard greenery important, however, in real situation it is not necessarily true. There is a discrepancy of the residents'expectation for the role of the greenery in the reality. Therefore, it is difficult to apply residents'evaluation directly to landscape planning. It is recommended that both planners'judgement and the residents'expectation should be balanced in the planning process.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.99-104
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• 2016

This paper aims to develop the One-In-One web solutions that can work in PC, tablet, notebook, and smart phone depending on the flow of the ICT times to promote the marketing of the technology. The characteristics of this web solutions can be used to a image viewer system of 3D array type for PC, tablet, notebook, and smart phone. It is implemented in the design of 3D display slide show type. And it is developed an image viewer system, which enables users to use by utilizing the links manner ICT base required by each channel of the image. This is the 3D photo viewer PR solution developed in the way that anyone can use easily without the knowledge of programming in various areas such as public relations, business, and education.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.70-81
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• 2001

This paper presents a 3D face modeling method using a CCD camera and a projector (LCD projector or Slide projector). The camera faces the human face and the projector casts white stripe patterns on the human face. The 3D shape of the face is extracted from spatial and temporal locations of the white stripe patterns on a series of image frames. The proposed method employs region segmentation and multi-beam techniques for efficient 3D modeling of hair region and faster 3D scanning respectively. In the proposed method, each image is segmented into face, hair, and shadow regions, which are independently processed to obtain the optimum results for each region. The multi-beam method， which uses a number of equally spaced stripe patterns, reduces the total number of image frames and consequently the overall data acquisition time. Light beam calibration is adopted for efficient light plane measurement, which is not influenced by the direction (vertical or horizontal) of the stripe patterns. Experimental results show that the proposed method provides a favorable 3D face modeling results, including the hair region.

• The Korean Journal of Malacology
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• v.26 no.3
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• pp.245-254
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• 2010

For the studies of germ cell development and maturation in the ovary, the gametogenic cycle and the number of spawning seasons per year in female Ruditapes philippinarum were investigated by quantitative statistical analysis using an Image Analyzer System. Compared with the results by qualitative and quantitative analyses, monthly variations in female gonad indice by qualitative histological analysis showed a pattern similar to that of the female gametogenic cycle calculated by quantitative statistical analysis. The number of spawning seasons occurred once per year, from June to October. In quantitative statistical analysis using an image analyzer system, monthly changes in the portions (%) of the ovary area to total tissue areas in females increased in March and reached a maximum in May, and then showed a rapid decrease from June to October when spawning occurred. And also monthly changes in portions (%) of follicle areas to the ovary area and in portions of oocyte areas to ovarian tissue areas in females began to increase in March and reached a maximum in May, and then. rapidly dropped from June to October when spawnig occurred. From these data, it is apparent that the number of spawning seasons occurred once per year, from June to October. Monthly changes in the number of the oocyte per $mm^2$ and in mean diameter of the oocyte in captured image which were calculated for each female slide showed a maximum in May and reached the minimum from December to February. Therefore, female R. philippinarum showed a unimodal gametogenic cycle during the year.