• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7785-7788
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• 2015

Utilization of high energy photons (>10MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6MV photons and then these calculations were repeated ten times with incorporating 18MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV ($D_{max}$) and the volume of CTV which covered with 95% Isodose line ($V_{CTV,95%IDL}$) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18MV photons was defined as the intersection point of $D_{max}$ and $V_{CTV,95%IDL}$ graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.273-280
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• 2016

Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.

• The Korean Journal of Nuclear Medicine
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• v.28 no.2
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• pp.172-176
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• 1994

As the Tc-99m-MIBI myocardial SPECT demonstrated wide application in the diagnosis of myocardial function, the quantitative and severity-dependent information is currently re quired. In this study, we proposed a computerized method for scoring the fixed defects in terms of extent-weighted severity and for identifying the reversibility in ischemic regions. At the first stage of this method, the transverse slices were reconstructed with 0.4 Nyquist freq. and order 5 Butterworth filter. From the oblique/sagittal slices, maximal count per pixel circumferential profiles were extracted for each sector, and then stress/redist. polar maps were normalized and plotted. For reversibility, the stress polar map was subtracted from the de-layed image and positive-valued pixels were categorized into three grades. The extent-weight-ed severity scores were calculated using the assigned grades and their number of pixels. This procedure was done automatically and the reversibility and severity scores were produced for each of the coronary territories (LAD, RCA, LCX) or any combination of these. Clinical ap-plication has shown that the changes In reversibility scores after PTCA were correlated linearly with the pre PTCA scores(r>0.8) in postinfarct cases as well as in angina, and severity scores of persistent defects in stress/rest SPECT study matched to the regional ejection fraction and visual analysis of regional wall motion of gated blood pool scan(r>0.6). We conclude that the computerized severity scoring method for the analysis of myocardial SPECT could be useful in the assessment of the myocardial ischemia and fixed defect.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.323-328
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• 2004

The development of group-specific tissue probability maps (TPM) provides a priori knowledge for better result of cerebral tissue classification with regard to the inter-ethnic differences of inter-subject variability. We present sequential procedures of group-specific TPM and evaluate the age effects in the structural differences of TPM. We investigated 100 healthy volunteers with high resolution MRI scalming. The subjects were classified into young (60, 25.92+4.58) and old groups (40, 58.83${\pm}$8.10) according to the age. To avoid any bias from random selected single subject and improve registration robustness, average atlas as target for TPM was constructed from skull-stripped whole data using linear and nonlinear registration of AIR. Each subject was segmented into binary images of gray matter, white matter, and cerebrospinal fluid using fuzzy clustering and normalized into the space of average atlas. The probability images were the means of these binary images, and contained values in the range of zero to one. A TPM of a given tissue is a spatial probability distribution representing a certain subject population. In the spatial distribution of tissue probability according to the threshold of probability, the old group exhibited enlarged ventricles and overall GM atrophy as age-specific changes, compared to the young group. Our results are generally consistent with the few published studies on age differences in the brain morphology. The more similar the morphology of the subject is to the average of the population represented by the TPM, the better the entire classification procedure should work. Therefore, we suggest that group-specific TPM should be used as a priori information for the cerebral tissue classification.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.803-810
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• 2013

The purpose of this research is to analyze the effect of shape of the inductive textile electrode on the non-contact heart activity sensing, based on the magnetic-induced conductivity principle. Four types of the inductive textile electrodes were determined according to the combinations of the two shape features. A fiber-metal hybrid-typed conductive thread was developed and applied to materialization of the textile electrodes by embroidery method. The heart activity was extracted through the textile electrode sewn on a T-shirt. The experiments were implemented to constantly measure the heart activity for 20 seconds, in each case of 5 healthy male subjects. The heart activity signals acquired in each type of the inductive textile electrode were analyzed, 1)by drawing a comparison of morphology with those of ECG signal (LeadII), and 2)by calculation of the normalized mean and standard deviation of magnitude of the heart activity signals. The analysis resulted that the relatively better quality of signals were acquired in the 'square' types in the matter of whole shape, while the better results were obtained in 'donut' types in the matter of center hole. Accordingly, the relatively best quality of signals was obtained in the case of 'Square-Donut' type of the inductive textile electrode.

• Journal of Life Science
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• v.25 no.4
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• pp.376-384
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• 2015

To investigate the molecular events of controlling intramuscular fat (or marbling), which is an important factor in the evaluation of beef quality, we performed cDNA microarray analyses using the longissimus dorsi muscle and back fat tissues. For this study, we constructed normalized cDNA libraries: fat tissues in native Korean cattle (displaying 1,211 specific genes), and muscle tissues in native Korean cattle (displaying 1,346 specific genes). A bovine cDNA chip was constructed with 1,680 specific genes, consisting of 760 genes from muscle tissues and 920 genes from fat tissues. The microarray analysis in this experiment showed a number of differentially expressed genes, which compared the longissimus dorsi muscle (Cy5) with back fat tissue (Cy3). Among many specific differentially expressed genes, 12-lipoxygenase (oxidizing esterified fatty acids) and prostaglandin D synthase (differentiation of fibroblasts to adipocytes) are the key candidate enzymes that should be involved in controlling the accumulation of intramuscular fat. In this study, differentially and commonly expressed genes in the muscle and fat tissues of native Korean cattle were found in large numbers, using the hybridization assay. The expression levels of the selected genes were confirmed by semi-quantitative RT-PCR, and the results were similar to those of the cDNA microarray.

• Journal of Chest Surgery
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• v.49 no.5
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• pp.329-336
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• 2016

Background: Guidelines for esophagogastroduodenoscopy (EGD) in the West allow the continued use of warfarin under therapeutic international normalized ratio (INR) level. In Korea, no guidelines have been issued regarding warfarin treatment before EGD. The authors surveyed Korean cardiac surgeons about how Korean cardiac surgeons handle warfarin therapy before EGD using a questionnaire. Participants were requested to make decisions regarding the continuation of warfarin therapy in two hypothetical cases. Methods: The questionnaire was administered to cardiac surgeons and consisted of eight questions, including two case scenarios. Results: Thirty- six cardiac surgeons at 28 hospitals participated in the survey, and 52.7% of the participants chose to stop warfarin before EGD in aortic valve replacement patients without risk factors for thromboembolism. When the patient's INR level was 2, 31% of the participants indicated that they would choose to continue warfarin therapy. For EGD with biopsy, 72.2% of the participants chose warfarin withdrawal, and 25% of the participants chose heparin replacement. In mitral valve replacement patients, 47.2% of the participants chose to discontinue warfarin, and 22.2% of the participants chose heparin replacement. For EGD with biopsy in patients with a mitral valve replacement, 58.3% of the participants chose to stop warfarin, and 41.7% of the participants chose heparin replacement. Conclusion: This study demonstrated that attitudes regarding warfarin treatment for EGD are very different among Korean surgeons. Guidelines specific to the Korean population are required.

• Korean Journal of Acupuncture
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• v.24 no.4
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• pp.99-110
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• 2007

Objectives : Epilepsy is one of the most common serious brain disorders that affect people of all ages, and it is characterized by recurrent unprovoked seizures. We examined whether acupuncture can reduce both the incidence of seizures and hippocampal cell death in dentate gyrus (DG) using a mouse model of kainic acid (KA)-induced epilepsy. Methods : ICR mice ($20{\sim}25$ g) were given acupuncture once a day at acupoint HT8 (sobu) bilaterally during 2 days before KA injection. After an intracerebroventricular injection of 0.1${\mu}g$ of KA, acupuncture treatment was subsequently administered once more (total 3 times), and the degree of seizure was observed for 20 min. Three hours after injection, we confirmed the neural cell death using cresyl violet staining and silver impregnation staining, and determined the expressions of c-Fos and glutamate decarboxylase (GAD)-67 using immunohistochemistry techniques in the DG. Results : KA induced epileptic seizure, neural cell death, increased c-Fos expression and decreased GAD-67 expression in the DG. Acupuncture treatment at HT8 reduced the severity of the epileptic seizure and inhibited neural cell death from KA. In addition, acupuncture normalized the expressions of c-Fos and GAD-67 in the same areas. Conclusions : These results demonstrated that acupuncture treatment at HT8 may reduce the KA-induced epileptic seizure and neural cell death in the DG possibly by normalizing c-Fos expressions and the gamma-aminobutyric acid neurons.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.