• Korean Journal of Radiology
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• v.23 no.12
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• pp.1260-1268
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• 2022

Objective: To propose standardized MRI-proton density fat fraction (PDFF) cutoff values for diagnosing hepatic steatosis, evaluated using contemporary PDFF measuring methods in a large population of healthy adults, using histologic fat fraction (HFF) as the reference standard. Materials and Methods: A retrospective search of electronic medical records between 2015 and 2018 identified 1063 adult donor candidates for liver transplantation who had undergone liver MRI and liver biopsy within a 7-day interval. Patients with a history of liver disease or significant alcohol consumption were excluded. Chemical shift imaging-based MRI (CS-MRI) PDFF and high-speed T2-corrected multi-echo MR spectroscopy (HISTO-MRS) PDFF data were obtained. By temporal splitting, the total population was divided into development and validation sets. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the MRI-PDFF method. Two cutoff values with sensitivity > 90% and specificity > 90% were selected to rule-out and rule-in, respectively, hepatic steatosis with reference to HFF ≥ 5% in the development set. The diagnostic performance was assessed using the validation set. Results: Of 921 final participants (624 male; mean age ± standard deviation, 31.5 ± 9.0 years), the development and validation sets comprised 497 and 424 patients, respectively. In the development set, the areas under the ROC curve for diagnosing hepatic steatosis were 0.920 for CS-MRI-PDFF and 0.915 for HISTO-MRS-PDFF. For ruling-out hepatic steatosis, the CS-MRI-PDFF cutoff was 2.3% (sensitivity, 92.4%; specificity, 63.0%) and the HISTO-MRI-PDFF cutoff was 2.6% (sensitivity, 88.8%; specificity, 70.1%). For ruling-in hepatic steatosis, the CS-MRI-PDFF cutoff was 3.5% (sensitivity, 73.5%; specificity, 88.6%) and the HISTO-MRI-PDFF cutoff was 4.0% (sensitivity, 74.7%; specificity, 90.6%). Conclusion: In a large population of healthy adults, our study suggests diagnostic thresholds for ruling-out and ruling-in hepatic steatosis defined as HFF ≥ 5% by contemporary PDFF measurement methods.

• Journal of Chest Surgery
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• v.42 no.3
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• pp.292-298
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• 2009

Background: Surgery for mitral valve disease in children carries both technical and clinical difficulties that are due to both the wide spectrum of morphologic abnormalities and the high incidence of associated cardiac anomalies. The purpose of this study is to assess the outcome of mitral valve surgery for treating congenital mitral regurgitation in children. Material and Method: From 1997 to 2007, 22 children (mean age: 5.4 years) who had congenital mitral regurgitation underwent mitral valve repair. The median age of the patients was 5.4 years old and four patients (18%) were under 12 months of age. 15 patients (68%) had cardiac anomalies. There were 13 cases of ventricular septal defect, 1 case of atrial septal defect and 1 case of supravalvar aortic stenosis. The grade of the preoperative mitral valve regurgitation was II in 4 patients, III in 15 patients and IV in 3. The regurgitation was due to leaflet prolapse in 12 patients, annular dilatation in 4 patients and restrictive leaflet motion in 5 patients. The preoperative MV Z-value and the regurgitation grade were compared with those obtained at follow-up. Result: MV repair was possible in all the patients. 19 patients required reduction annuloplasty and 18 patients required valvuloplasty that included shortening of the chordae, papillary muscle splitting, artificial chordae insertion and cleft closure. There were no early or late deaths. The mitral valve regurgitation after surgery was improved in all patients (absent=10, grade I=5, II=5, III=2). MV repair resulted in reduction of the mitral valve Z-value ($2.2{\pm}2.1$ vs. $0.7{\pm}2.3$, respectively, p<0.01). During the mid-term follow-up period of 3.68 years, reoperation was done in three patients (one with repair and two with replacement) and three patients showed mild progression of their mitral reguration. Conclusion: our experience indicates that mitral valve repair in children with congenital mitral valve regurgitation is an effective and reliable surgical method with a low reoperation rate. A good postoperative outcome can be obtained by preoperatively recognizing the intrinsic mitral valve pathophysiology detected on echocardiography and with the well-designed, aggressive application of the various reconstruction techniques.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.123-126
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• 2012

This paper proposed a method to separate a liver into left and right liver lobes for simple and exact volumetry of the river graft at abdominal MDCT(Multi-Detector Computed Tomography) image before the living donor liver transplantation. A medical team can evaluate an accurate river graft with minimized interaction between the team and a system using this algorithm for ensuring donor's and recipient's safe. On the image of segmented liver, 2 points(PMHV: a point in Middle Hepatic Vein and PPV: a point at the beginning of right branch of Portal Vein) are selected to separate a liver into left and right liver lobes. Middle hepatic vein is automatically segmented using PMHV, and the cutting line is decided on the basis of segmented Middle Hepatic Vein. A liver is separated on connecting the cutting line and PPV. The volume and ratio of the river graft are estimated. The volume estimated using 2 points are compared with a manual volume that diagnostic radiologist processed and estimated and the weight measured during surgery to support proof of exact volume. The mean ${\pm}$ standard deviation of the differences between the actual weights and the estimated volumes was $162.38cm^3{\pm}124.39$ in the case of manual segmentation and $107.69cm^3{\pm}97.24$ in the case of 2 points method. The correlation coefficient between the actual weight and the manually estimated volume is 0.79, and the correlation coefficient between the actual weight and the volume estimated using 2 points is 0.87. After selection the 2 points, the time involved in separation a liver into left and right river lobe and volumetry of them is measured for confirmation that the algorithm can be used on real time during surgery. The mean ${\pm}$ standard deviation of the process time is $57.28sec{\pm}32.81$ per 1 data set ($149.17pages{\pm}55.92$).

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.5
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• pp.265-277
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• 2019

Using the measured data of waves and shore-line, we reviewed the grand circulation process and seasonal variation of beach cusp at the Mang-Bang beach from the perspective of trapped mode Edge waves known as the driving mechanism of beach cusp. In order to track the temporal and spatial variation trends of beach cusp, we quantify the beach cusp in terms of its wave length and amplitude detected by threshold crossing method. In doing so, we also utilize the spectral analysis method and its associated spectral mean sand wave number. From repeated period of convergence and ensuing splitting of sand waves detected from the yearly time series of spectral mean sand wave number of beach cusp, it is shown that the grand circulation process of beach cusp at Mang-Bang beach are occurring twice from 2017. 4. 26 to 2018. 4. 20. For the case of beach area, it increased by $14,142m^2$ during this period, and the shore-line advanced by 18 m at the northen and southern parts of the Mang-Bang beach whereas the shore-line advanced by 2.4 m at the central parts of Mang-Bang beach. It is also worthy of note that the beach area rapidly increased by $30,345m^2$ from 2017.11.26. to 2017.12.22. which can be attributed to the nature of coming waves. During this period, mild swells of long period were prevailing, and their angle of attack were next to zero. These characteristics of waves imply that the main transport mode of sediment would be the cross-shore. Considering the facts that self-healing capacity of natural beaches is realized via the cross-shore sediment once temporarily eroded. it can be easily deduced that the sediment carried by the boundary layer streaming toward the shore under mild swells which normally incident toward the Mang-Bang beach makes the beach area rapidly increase from 2017.11.26. to 2017.12.22.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.801-809
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• 2019

Existing Gamma Knife Radiosurgery(GKRS) for large lesions is often conducted in stages with volume or dose partitions. Often in case of volume division the target used to be divided into sub-volumes which are irradiated under the determined prescription dose in multi-sessions separated by a day or two, 3~6 months. For the entire course of treatment, treatment informations of the previous stages needs to be reflected to subsequent sessions on the newly mounted stereotactic frame through coordinate transformation between sessions. However, it is practically difficult to implement the previous dose distributions with existing Gamma Knife system except in the same stereotactic space. The treatment area is expanding because it is possible to perform the multistage treatment using the latest Gamma Knife Platform(GKP). The purpose of this study is to introduce the image-coregistration based on the stereotactic spaces and the strategy of multistage GKRS such as the determination of prescription dose at each stage using new GKP. Usually in image-coregistration either surgically-embedded fiducials or internal anatomical landmarks are used to determine the transformation relationship. Author compared the accuracy of coordinate transformation between multi-sessions using four or six anatomical landmarks as an example using internal anatomical landmarks. Transformation matrix between two stereotactic spaces was determined using PseudoInverse or Singular Value Decomposition to minimize the discrepancy between measured and calculated coordinates. To evaluate the transformation accuracy, the difference between measured and transformed coordinates, i.e., ${\Delta}r$, was calculated using 10 landmarks. Four or six points among 10 landmarks were used to determine the coordinate transformation, and the rest were used to evaluate the approaching method. Each of the values of ${\Delta}r$ in two approaching methods ranged from 0.6 mm to 2.4 mm, from 0.17 mm to 0.57 mm. In addition, a method of determining the prescription dose to give the same effect as the treatment of the total lesion once in case of lesion splitting was suggested. The strategy of multistage treatment in the same stereotactic space is to design the treatment for the whole lesion first, and the whole treatment design shots are divided into shots of each stage treatment to construct shots of each stage and determine the appropriate prescription dose at each stage. In conclusion, author confirmed the accuracy of prescribing dose determination as a multistage treatment strategy and found that using as many internal landmarks as possible than using small landmarks to determine coordinate transformation between multi-sessions yielded better results. In the future, the proposed multistage treatment strategy will be a great contributor to the frameless fractionated treatment of several Gamma Knife Centers.

• Journal of Chest Surgery
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• v.35 no.10
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• pp.705-711
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• 2002

The advantages of mitral valve reconstruction have been well established and so mitral valve reconstruction is now considered as the procedure of choice to correct mitral valve disease. This is the report of intermediate-term results of 38 cases that performed mitral valve reconstruction for valve insufficiency(the total number of mitral valve reconstruction were 49 cases, but 11 cases that performed mitral valve replacement due to incomplete reconstruction were excluded). Material and Method : From March 1991 to March 2001, 38 patients underwent mitral valve repair due to mitral valve regurgitation with or without stenosis. Mean age was 47.6$\pm$14.7 years(range 15 to 70 years) : 11 were men and 27 were women. The causes of mitral valve regurgitation were degenerative in 14, rheumatic in 21, infective in 2 and the other was congenital. Result : According to the Carpentier's pathologic classification of mitral valve regurgitation, 3 were type 1 , 16 were type II and 19 were type III. Surgical procedures were annuloplasty 15, commissurotomy 19, leaflet resection and annular plication 9, chordae shortening 11, chordae transfer 5, new chordae formation 2, papillary muscle splitting 2 and vegetectomy 2. These procedures were combined in most patients. There were 2 early death and the causes of death were respiratory failure, renal failure and sepsis. There was no late death. Valve replacement was done in 6 patients after repair due to valve insufficiency or stenosis 3 weeks, 1, 3, 51, 69, 84months later respectively. These patients have been followed up from 1 to 116 months(mean 43.0 months). The mean functional class(NYHA) was 2.36 pre-operatively and improved to 1.70. Conclusion : In most cases of mitral valve regurgitation, mitral valve reconstruction when technically feasible is effective operation that can achieve stable functional results and low surgical and late mortality.

• Journal of Dairy Science and Biotechnology
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• v.27 no.2
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• pp.55-62
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• 2009

Milk is called an almost complete food in terms of nutrition, especially for the younger generations because it contains a number of nutrients required for growth and development. Lactose intolerance is defined as a malabsorption of lactose in the intestine with some typical symptoms of abdominal pains and bloating, and occurred at 75% of global populations, which hampers milk consumption worldwide. Lacks of milk consumption in the underdeveloped countries frequently lead to many nutrients deficiencies, so that diseases including osteoporosis, hypertension, and colon cancer are more prevalent in the recent days. Lactose in foods needs to be hydrolyzed prior to intestinal absorption. The hydrolytic enzyme responsible for splitting lactose into its monomeric forms, glucose and galactose, is called as lactase or $\beta$-galactosidase. The former is primarily used as blood sugar and energy source and the latter used in glycolipid synthesis of brain tissues in infants. Lactose is clinically diagnosed with the breath hydrogen production test as well as intestinal biopsy. Reportedly, symptoms of lactose intolerance are widely prevalent at 25% of Europeans, 50 to 80% of Hispanics, South Indians, Africans, and Jews, almost 100% of Asians and native Americans. For the adults, phenotype of lactase persistence, which is able to hydrolyse lactose, is more common in the northern Europeans, but in the other area lactase non-persistence or adult-type hypolactasia is dominant. Genetic analysis on human lactase gene continued that lactase persistence was closely related to the err site of 1390 single nucleotide polymorphism from the 5'-end. To alleviate severity of lactose intolerance symptoms, some eating patterns including drinking milk a single cup or less, consumption along with other foods, whole milk rather than skimmed milk, and drink with live yogurt cultures, are highly recommended for the lactose maldigesters. Also, delay of gastric emptying is effective to avoid the symptoms from lactose intolerance. Frequency of lactose intolerance with conventional diagnosis is thought overestimated mainly because the subjects are exposed to too much lactose of 50 g rather than a single serving amount. Thus simple and accurate diagnostic method for lactose intolerance need to be established. It is thought that fermented milk products and low- or free lactose milks help improve currently stagnant milk consumption due to lactose intolerance which contributes to major barrier in milk marketing especially in Asian countries.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.