• 패션비즈니스
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• 제25권2호
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• pp.34-50
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• 2021

As silver surfers'(older people who are good at surfing the Internet) SNS use and influence increase, SNS shopping channels are also favored as the major means of contactless shopping. The study analyzed the trends in fashion styles corresponding to taste on the SNS shopping channels with silver surfer women as the target. Even though the shopping channel was for people in their 50s and 60s, most of the design factors focused on casual styles and young taste rather than the formal and mature image. The material, trim, and patterns reflected a retro or formal factor but were also reinterpreted as a young image to show the various forms. The characteristics of the women's fashion styles for silver surfers on the SNS shopping channels can be summarized as follows. First, they chose items that portrayed a young image regardless of their age. The colors, materials, and patterns of the products also helped to create a young image. Second, there was a tendency to pursue various casual items. Silver surfer women chose practical, casual items because of their active lifestyle. Third, retro items were reinterpreted as young and trendy. Silver surfer women showed their retro tastes but preferred practical clothing with young images.

• Investigative Magnetic Resonance Imaging
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• 제19권2호
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• pp.67-75
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• 2015

Purpose: To investigate the value of image post-processing software (FreeSurfer, IBASPM [individual brain atlases using statistical parametric mapping software]) and inversion time (TI) in volumetric analyses of the hippocampus and to identify differences in comparison with manual tracing. Materials and Methods: Brain images from 12 normal adults were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) with a slice thickness of 1.3 mm and TI of 800, 900, 1000, and 1100 ms. Hippocampal volumes were measured using FreeSurfer, IBASPM and manual tracing. Statistical differences were examined using correlation analyses accounting for spatial interpretations percent volume overlap and percent volume difference. Results: FreeSurfer revealed a maximum percent volume overlap and maximum percent volume difference at TI = 800 ms ($77.1{\pm}2.9%$) and TI = 1100 ms ($13.1{\pm}2.1%$), respectively. The respective values for IBASPM were TI = 1100 ms ($55.3{\pm}9.1%$) and TI = 800 ms ($43.1{\pm}10.7%$). FreeSurfer presented a higher correlation than IBASPM but it was not statistically significant. Conclusion: FreeSurfer performed better in volumetric determination than IBASPM. Given the subjective nature of manual tracing, automated image acquisition and analysis image is accurate and preferable.

• 정보관리연구
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• 제31권4호
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• pp.50-70
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• 2000

인터넷으로 접근할 수 정보의 형태가 텍스트는 물론 이미지나 사운드까지 포함되면서 다양한 웹 이미지 검색엔진들이 개발되고 있다. 그러나 이 검색엔진들은 검색 특성과 효율성 면에서 상당한 차이를 보이고 있다. 이에 본 연구에서는 현재 개발된 이미지정보를 탐색하는 검색엔진들의 유형을 살펴보고 이들의 특성과 성능을 비교 평가하여 이용자로 하여금 정보요구에 적합한 이미지 검색엔진을 선택할 수 있도록 하는데 그 목적이 있다. 본 연구의 비교대상 검색엔진으로는 현재 가장 널리 쓰이고 있는 AV Photo Finder, Lycos MultiMedia, Amazing Picture Machina Image Surfer, WebSeek, Ditto를 선정하였다. 먼저 문헌연구를 통해 이미지 검색엔진의 평가기준을 마련하였다. 그리고 마련된 기준에 따라 각 검색엔진들의 데이터베이스 및 색인 방법, 검색 기능, 출력 형태, 이용자 인터페이스를 조사하였고 검색성능을 평가하기 위해 상대적 재현율과 정확률을 측정하였다. 그 결과 AV Photo Finder의 정확률이 가장 높았고 Ditto와 WebSeek의 정확률은 비교적 높은 편이었다. 그리고 Lycos MultiMedia와 Image Surfer의 정확률 값이 그 뒤를 이었으며 Amazing Picture Machine의 정확율이 가장 낮았다.

• 스마트미디어저널
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• 제12권7호
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• 생물정신의학
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• 제26권1호
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• pp.22-31
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• 2019

Objectives Previous studies have revealed inconsistent results on amygdala volume in adult bipolar disorder (BD) patients compared to healthy controls (HC). Since the amygdala encompasses multiple subregions, the subtle volume changes in each amygdala nucleus might have not been fully reflected in the measure of the total amygdala volume, causing discrepant results. Thus, we aimed to investigate volume changes in each amygdala subregion and their association with subtypes of BD, lithium use and clinical status of BD. Methods Fifty-five BD patients and 55 HC underwent T1-weighted structural magnetic resonance imaging. We analyzed volumes of the whole amygdala and each amygdala subregion, including the anterior amygdaloid area, cortico-amygdaloid transition area, basal, lateral, accessory basal, central, cortical, medial and paralaminar nuclei using the atlas in the FreeSurfer. The volume difference was analyzed using a one-way analysis of covariance with individual volumes as dependent variables, and age, sex, and total intracranial volume as covariates. Results The volumes of whole right amygdala and subregions including basal nucleus, accessory basal nucleus, anterior amygdaloid area, and cortico-amygdaloid transition area in the right amygdala of BD patients were significantly smaller for the HC group. No significant volume difference between bipolar I disorder and bipolar II disorder was found after the Bonferroni correction. The trend of larger volume in medial nucleus with lithium treatment was not significant after the Bonferroni correction. No significant correlation between illness duration and amygdala volume, and insignificant negative correlation were found between right central nucleus volume and depression severity. Conclusions Significant volume decrements of the whole amygdala, basal nucleus, accessory basal nucleus, anterior amygdaloid area, and cortico-amygdaloid transition area were found in the right hemisphere in adult BD patients, compared to HC group. We postulate that such volume changes are associated with altered functional activity and connectivity of amygdala nuclei in BD.