• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• Journal of Korean Neurosurgical Society
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• v.30 no.4
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• pp.522-527
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• 2001

We present a case of recurrent extraventricular neurocytoma with malignant glial differentiation in left temporoparietal area. A 37-year-old man with presentation of generalized seizure had undergone biopsy of brain tumor in left parietal area in 1987, which revealed extraventricular neurocytoma and radiotherapy was followed. Postoperative course was uneventful until eleven years after biopsy, when he became gradually aphasic and right hemiplegic. Brain CT and MRI revealed enlargement of tumor with peritumoral edema and calcifications. He underwent subtotal tumor removal in 1998. Microscopic examination of second biopsy specimen revealed presence of large areas composed of anaplastic glial cells with frequent mitosis, nuclear pleomorphism, large eosinophilic cytoplasm and eccentric nuclei, resembling gemistocytes, which were strongly immunoreactive to glial fibrillary acidic protein(GFAP) but not to synaptophysin(SNP). Also focal areas of neuronal cells were found, which were immunoreactive to SNP but not to GFAP. These histologic findings imply that this recurred tumor was a high grade, mixed tumor with divergent differentiation of neuronal and astrocyte lineage. We report a rare case of extraventricular cerebral neurocytoma with malignant glial differentiation with review of the literature.

• Journal of Yeungnam Medical Science
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• v.15 no.1
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• pp.114-124
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• 1998

We analyzed the mammographic (n=21) findings (location, margin, shape, cluster microcalcifications, size, multiplicity) and ultrasonographic (n=12) findings (shape, border, internal echo, boundary echo, posterior echo, lateral echo, width/depth ratio) to evaluate specific radiologic findings of histopathologically proved uncommon breast cancer. The mammographic findings (n=21) are as follow; 1) single; 16, multiple; 5 2) margin (smooth; 13, irregular; 4, spiculated; 4) 3) shape (round and ovoid; 9, lobulated; 8, irregular; 4) 4) cluster micro calcifications (abscent; 20, present; 1) 5) size (1-3cm; 18, 3-5cm; 2, 5cm> ; 1) 6) location (UOQ; 13, UIQ; 4, LIQ; 3, LOQ; 1). The ultrasonographic findings (n=12) are as follow; 1) shape (round to oval; 5, lobulated; 5, irregular; 2) 2) border (smooth even; 9, rough uneven; 3) 3) internal echo (fine homogeneous; 5, coarse heterogeneous; 7) 4) boundary echo (regular fine; 4, irregular thick; 8) 5) posterior echo (enhanced; 11, no change; 1) 6) lateral echo (marked; 7, nonexistent; 5) 7) width/depth ratio (1.5> ; 1, 1.0-1.5; 7, 1.0< ; 4). Uncommon breast cancer show benign nature on mammogram, but malignant nature on ultrasonogram (especially boundary echo, internal echo, width/depth ratio).