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Comparison of Ultrasound Image Quality using Edge Enhancement Mask (경계면 강조 마스크를 이용한 초음파 영상 화질 비교)

  • Jung-Min, Son;Jun-Haeng, Lee
    • Journal of the Korean Society of Radiology
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    • v.17 no.1
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    • pp.157-165
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    • 2023
  • Ultrasound imaging uses sound waves of frequencies to cause physical actions such as reflection, absorption, refraction, and transmission at the edge between different tissues. Improvement is needed because there is a lot of noise due to the characteristics of the data generated from the ultrasound equipment, and it is difficult to grasp the shape of the tissue to be actually observed because the edge is vague. The edge enhancement method is used as a method to solve the case where the edge surface looks clumped due to a decrease in image quality. In this paper, as a method to strengthen the interface, the quality improvement was confirmed by strengthening the interface, which is the high-frequency part, in each image using an unsharpening mask and high boost. The mask filtering used for each image was evaluated by measuring PSNR and SNR. Abdominal, head, heart, liver, kidney, breast, and fetal images were obtained from Philips epiq5g and affiniti70g and Alpinion E-cube 15 ultrasound equipment. The program used to implement the algorithm was implemented with MATLAB R2022a of MathWorks. The unsharpening and high-boost mask array size was set to 3*3, and the laplacian filter, a spatial filter used to create outline-enhanced images, was applied equally to both masks. ImageJ program was used for quantitative evaluation of image quality. As a result of applying the mask filter to various ultrasound images, the subjective image quality showed that the overall contour lines of the image were clearly visible when unsharpening and high-boost mask were applied to the original image. When comparing the quantitative image quality, the image quality of the image to which the unsharpening mask and the high boost mask were applied was evaluated higher than that of the original image. In the portal vein, head, gallbladder, and kidney images, the SNR, PSNR, RMSE and MAE of the image to which the high-boost mask was applied were measured to be high. Conversely, for images of the heart, breast, and fetus, SNR, PSNR, RMSE and MAE values were measured as images with the unsharpening mask applied. It is thought that using the optimal mask according to the image will help to improve the image quality, and the contour information was provided to improve the image quality.

The Impact of e-Store Personality on e-Store Loyalty-Focus on the Mediating Role of Identification, Trust, and Engagement (온라인에서 점포 개성이 점포 충성도에 미치는 영향-동일시, 신뢰, 인게이지먼트의 매개 역할을 중심으로)

  • Park, Hyo-Hyun;Jung, Gang-Ok;Lee, Seung-Chang
    • Journal of Distribution Research
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    • v.16 no.2
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    • pp.57-94
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    • 2011
  • Nowadays, it is common that most consumers are purchasing goods in e-stores. The e-stores eager to attract, revisit, retain, and finally convert them into loyal customers. The e-store marketers have planned and executed numerous marketing efforts. As one of the marketing activities, e-store managers attempt to build web sites that meet customers' functional and psychological needs. A wide array of studies has been done to identify factors that could affect customers' response of web sites. Majority of studies were conducted to verify technology-related and functional variables of the website which facilitate transactions and enhance customer responses such as purchase intention and website loyalty. However, there has been little research on the external cues of website and psychological variables of consumer that could have positive influences on customer response. The purpose of this study is to investigate the influence of e-store personality on e-store loyalty through mediating variables such as e-store identification, e-store trust, and e-store engagement. The authors of this study develop the model and set up the six main hypotheses and a set of sub-hypotheses based on a literature review, shown in

    . This model is composed of four paths such as dimensions of e-store personality${\rightarrow}$e-store identification, e-store identification${\rightarrow}$e-store loyalty, e-store identification ${\rightarrow}$e-store trust${\rightarrow}$e-store loyalty, and e-store identification${\rightarrow}$e-store engagement${\rightarrow}$e-store loyalty. II. Research Method Ladies under 30s were the respondents of this survey. Data were collected from January 20th to February 26th in 2010. A total of 200 questionnaires were distributed and 169 respondents were analysed finally to test hypotheses because 31 questionnaires had incorrect or missing responses. SPSS 12.0 and LISREL 7.0 program were used to test frequency, reliability, factor, and structural equation modeling analysis. III. Result and Conclusion According to results from factor analysis, eigen value was over 1.0 and items which were below 0.6 were deleted. Consequently, 9 factors(% of total variance is 72.011%) were searched. All Cronbach's ${\alpha}$ values are over the recommended level(${\alpha}$ > 0.7). The overall fit indices are acceptable such as ${\chi}^2$=2028.36(p=0.00), GFI=0.87, AGFI=0.82, CFI=0.81, IFI=0.92, RMR=0.075. All factor loadings were over the recommended level. As the result of discriminant validity check with chi-square difference test between paired constructs, each construct has good discriminant validity. The overall fit indices of final model are acceptable such as ${\chi}^2$=340.73(df=36, p=0.00), GFI=0.92, AGFI=0.81, CFI=0.91, IFI=0.91, RMR=0.085. As test results, 5 out of 6 hypotheses are supported because there are statistically significant casual relationships in structural equation model, shown in . First of all, hypothesis 1 is partially supported because sub-hypothesis 1-1 and 1-2 are supported, whereas sub-hypothesis 1-3, 1-4, and 1-5 are rejected. Specifically, it reveals that warmth and sophistication dimensions in e-store personality have positive influence on e-store identification, however, activity, progressiveness, and strictness does not have any significant relationship on e-store identification. Secondly, hypothesis 2 was supported. Therefore, it can be said that e-store identification has a positive impact on e-store trust. Thirdly, hypothesis 3 is also supported. Hence, there is a positive relationship between e-store identification and e-store engagement. Fourthly, hypothesis 4 is supported too. e-store identification has a positive influence on e-store loyalty. Fifthly, hypothesis 5 is also accepted. This indicates that e-store trust is a precedent variable which positively affects e-store loyalty. Lastly, it reveals that e-store engagement has a positive impact on e-store loyalty. Therefore, hypothesis 6 is supported. The findings of the study imply that some dimensions of e-store personality have a positive influence on e-store identification, and that e-store identification has direct and indirect influence on e-store loyalty through e-store trust and e-store engagement positively. These results also suggest that the e-store identification in e-store personality is a precedent variable which positively affects e-store loyalty directly and indirectly through e-store trust and engagement as a mediating variable. Therefore, e-store marketers need to implement website strategy based on e-store personality, e-store identification, e-store trust, and e-store engagement to meet customers' psychological needs and enhance e-store loyalty. Finally, the limitations and future study directions based on this study are discussed.

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