• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.393-402
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• 2007

The purpose of this study is to provide the design direction based on consumer sensitivity through the structure between product design preferences - sensitivity image - design elements. For the purpose, we selected men's shaver products for this study subject and collected 164 shavers' pictures released between 2001-2007 years. Then, we carried out a pilot test for collection of sensitivity images about shavers, made a survey using semantic differential method and analyzed the survey. According the result, consumers preferred the sensitivity images "luxury, attractive, stable", design elements satisfied the preference images were "form of body is not a circular arcs or a polygon, material is steel, button is push style, and a color of body is not brown." This study can provide a base of the causal relationship between design preferences - sensitivity image - design elements and a design process to predict consumer sensitivity-oriented design.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1289-1299
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• 2021

Objective: We aimed to evaluate the usefulness of arterial subtraction images for predicting the viability of hepatocellular carcinoma (HCC) after locoregional therapy (LRT) using gadoxetic acid-enhanced MRI and the Liver Imaging Reporting and Data System treatment response (LR-TR) algorithm. Materials and Methods: This study included 90 patients (mean age ± standard deviation, 57 ± 9 years) who underwent liver transplantation or resection after LRT and had 73 viable and 32 nonviable HCCs. All patients underwent gadoxetic acid-enhanced MRI before surgery. Two radiologists assessed the presence of LR-TR features, including arterial phase hyperenhancement (APHE) and LR-TR categories (viable, nonviable, or equivocal), using ordinary arterial-phase and arterial subtraction images. The reference standard for tumor viability was surgical pathology. The sensitivity of APHE for diagnosing viable HCC was compared between ordinary arterial-phase and arterial subtraction images. The sensitivity and specificity of the LR-TR algorithm for diagnosing viable HCC was compared between the use of ordinary arterial-phase and the use of arterial subtraction images. Subgroup analysis was performed on lesions treated with transarterial chemoembolization (TACE) only. Results: The sensitivity of APHE for viable HCCs was higher for arterial subtraction images than ordinary arterial-phase images (71.2% vs. 47.9%; p < 0.001). LR-TR viable category with the use of arterial subtraction images compared with ordinary arterial-phase images showed a significant increase in sensitivity (76.7% [56/73] vs. 63.0% [46/73]; p = 0.002) without significant decrease in specificity (90.6% [29/32] vs. 93.8% [30/32]; p > 0.999). In a subgroup of 63 lesions treated with TACE only, the use of arterial subtraction images showed a significant increase in sensitivity (81.4% [35/43] vs. 67.4% [29/43]; p = 0.031) without significant decrease in specificity (85.0% [17/20] vs. 90.0% [18/20]; p > 0.999). Conclusion: Use of arterial subtraction images compared with ordinary arterial-phase images improved the sensitivity while maintaining specificity for diagnosing viable HCC after LRT using gadoxetic acid-enhanced MRI and the LR-TR algorithm.

• Asia pacific journal of information systems
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• v.31 no.3
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• pp.335-357
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• 2021

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• The Journal of Korean Medicine
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• v.44 no.4
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• pp.136-149
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• 2023

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.36-42
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• 2005

Purpose : To develop a novel approach to calculate the sensitivity profiles of the phased array coil for use in non-uniform intensity correction. Materials and Methods : The proposed intensity correction method estimates the sensitivity profile of the coil to extract intensity variations that represent the scanned image. The sensitivity profile is estimated by fitting a non-linear curve to various angles of projections through the imaged object in order to eliminate the high-frequency image content. Filtered back projection is then used to compute the estimates of the sensitivity profile of each coil. The method was applied both to phantom and brain images from 8-channel phased-array coil and 4-channel phased-array coil, respectively. Results : Intensity-corrected images from the proposed method have more uniform intensity than those from the commonly used 'sum-of-squares' approach. By using the proposed correction method, the intensity variation was reduced to $6.1\%$ from $13.1\%$, acquired from the 'sum-of-squares'. Conclusion : The proposed method is more effective at correcting the intensity non-uniformity of the phased-array surface-coil images than the conventional 'sum-of-squares' method.

• Imaging Science in Dentistry
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• v.53 no.4
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• pp.375-381
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• 2023

Purpose: This study assessed the diagnostic performance of stitched and non-stitched cross-sectional cone-beam computed tomography (CBCT) images of non-displaced ovine mandibular fractures. Materials and Methods: In this ex vivo study, non-displaced fractures were artificially created in 10 ovine mandibles (20 hemi-mandibles) using a hammer. The control group comprised 8 hemi-mandibles. The non-displaced fracture lines were oblique or vertical, <0.5 mm wide, 10-20 mm long, and only in the buccal or lingual cortex. Fracture lines in the ramus and posterior mandible were created to be at the interface or borders of the 2 stitched images. CBCT images were obtained from the specimens with an 80 mm×80 mm field of view before and after fracture induction. OnDemand software (Cybermed, Seoul, Korea) was used for stitching the CBCT images. Four observers evaluated 56 (28 stitched and 28 non-stitched) images to detect fracture lines. The diagnostic performance of stitched and non-stitched images was assessed by calculating the area under the receiver operating characteristic curve (AUC). Sensitivity and specificity values were also calculated (alpha=0.05). Results: The AUC was calculated to be 0.862 and 0.825 for the stitched and non-stitched images, respectively (P=0.747). The sensitivity and specificity were 90% and 75% for the non-stitched images and 85% and 87% for the stitched images, respectively. The inter-observer reliability was shown by a Fleiss kappa coefficient of 0.79, indicating good agreement. Conclusion: No significant difference was found in the diagnostic performance of stitched and non-stitched cross-sectional CBCT images of non-displaced fractures of the ovine mandible.

• Publications of The Korean Astronomical Society
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• v.25 no.1
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• pp.15-21
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• 2010

We report results of image simulations of the KVN and VLBI experiments of the KVN with several other East Asia VLBI facilities. To investigate their imaging capability a model-generated image of 7 mm SiO maser emission in Mira variables is used. The resulting simulations show that the joint VLBI experiments of the KVN with East Asia VLBI facilities can produce reasonably good images at 7 mm spectral line experiments. However, there are no apparent differences in peak flux densities and images themselves in the simulations among different combinations of these facilities. In addition, the simulated images of observations which include bigger antennas do not show any expected improvement to the image sensitivity. The small variations in the peak flux density and similar image sensitivity, irrespective of different antenna sizes or numbers of baselines used in the simulations, turn out due to a specific characteristic of the adopted model image. Test simulations using another SiO maser image from R Cas observations prove that the participation of bigger antennas in the VLBI experiments does improve image sensitivity. We confirm the need of additional longer baselines in the experiments of the East Asia VLBI facilities to study very compact maser clumps on sub-milliarcsecond scales.

• Imaging Science in Dentistry
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• v.32 no.3
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• pp.123-127
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• 2002

Purpose : To determine whether subtraction images utilizing contrast media can improve the diagnostic performance of proximal caries diagnosis compared to conventional periapical radiographic images. Materials and Methods: Thirty-six teeth with 57 proximal surfaces were radiographied using a size #2 RVG-ui sensor (Trophy Radiology, Marne-la-Vallee, France). The teeth immersed in water-soluble contrast media and subtraction images were taken. Each tooth was then sectioned for histologic examination. The digital radiographic images and subtraction images were examined and interpreted by three dentists for proximal caries. The results of the proximal caries diagnosis were then verified with the results of the histologic examination. Results: The proximal caries sensitivity using digital subtraction radiography was significantly higher than simply examining a single digital radiograph. The sensitivity of the proximal dentinal carious lesion when analyzed with the subtraction radiograph and the radiograph together was higher than with the subtraction radiograph or the radiograph alone. Conclusion: The use of subtraction radiography with contrast media may be useful for detecting proximal dentinal carious lesions.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.97-102
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• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.