• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.812-819
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• 1996

This study was performed to quantify microscopically morphological characteristics of cotton fiber to identify parameters for quality evaluation using image analysis . The image of each fiber was captured by a Pc-based color imaging system using a conventional microscope. Ends of individual cotton fibers were glued on a microscope slide without any tension or straightening. A modified watershed technique was implemented to identify individual convolution segments, which were defined as sections of the fiber bordered by two neighboring convolutions. Length, area and perimeter of each convolution segment were measured directly from the image . Average width, shape factor and number of convolution segments in mm were calculated from the measured parameters. The performance of the image analysis algorithm was compared with visual varieties of cotton . The image analysis results agreed with visual inspection in 89.6% of the tested images.

• Current Optics and Photonics
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• v.8 no.4
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• pp.327-344
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• 2024

Optical phantoms are essential in optical imaging and measurement instruments for performance evaluation, calibration, and quality control. They enable precise measurement of image resolution, accuracy, sensitivity, and contrast, which are crucial for both research and clinical diagnostics. This paper reviews the recent advancements and challenges in phantoms for optical coherence tomography, photoacoustic imaging, digital holographic microscopy, optical diffraction tomography, and oximetry tools. We explore the fundamental principles of each technology, the key factors in phantom development, and the evaluation criteria. Additionally, we discuss the application of phantoms used for enhancing optical-image quality. This investigation includes the development of realistic biological and clinical tissue-mimicking phantoms, emphasizing their role in improving the accuracy and reliability of optical imaging and measurement instruments in biomedical and clinical research.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.533-540
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• 2002

We can buy products everywhere from Web-based shopping mall using desktop, cellular phone or PDA. To guarantee the various services for different equipment, shopping mail systems should allow a lot of different size or quality of images to provide a good service to their customers. Despite of same product image, each shopping mall saves the image in its storage space individually. Furthermore, all the product images in each shopping mall are stored as images of different quality. It may waste resources of shopping mail server and bring us developmental overhead. It is difficult to update all the images for product that is used by distributed e-catalog in everywhere. In this paper, we extended the proposed Web Image Server (WIS) for sharing one image with all clients and processing Image dynamically, so that we strengthened the function of managing shopping mall as a client of WIS and added the function of recording clients'log file and image catalog for shopping mall. We measured the response time from WIS and conventional e-Commerce site using by WAS which is one of the stress test tools for Web application. Furthermore, we measured WIS responses image requests in reasonable time when the current user is increased.

• Journal of the Korean Society of Food Culture
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• v.28 no.2
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• pp.195-203
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• 2013

The aim of this study was to provide strategic implications for the coffee market in which competition got severer through IPA analysis based on the attributes of selection of a specialized franchise coffee shop. The results of a positive analysis conducted with undergraduates in their twenties were as follows: According to the evaluation of the importance of the attributes of selection of a specialized coffee shop, the most important were 'cleanliness and hygienic facilities inside the shop (6.09)' and 'taste and quality of the menu (coffee) (6.09)'. According to the performance analysis, those showing the highest performance were 'brand image (4.92)' and 'cleanliness and hygienic facilities inside the shop (4.92)'. According to the result of IPA analysis, what customers regarded as being the most important were 'taste and quality of the menu (coffee)', 'kindness of the staff', and 'cleanliness and hygienic facilities inside the shop', and, in fact, they showed great performances as well. However, 'price of the menu (coffee)' was regarded as being important but did not indicate a great performance; therefore, they showed dissatisfaction with it. Although they did not think 'environment around the shop', 'brand image', 'brand recognition' or 'interior size and scale of the shop' were important, the attributes did not appear to show great performances. Therefore, we need constant maintenance strate gies regarding the fact that consumers are considered to be very important for evaluation, and should make efforts to change the price in advance.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.301-306
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• 2021

The use of tin filters as a way to reduce the medical radiation in computed tomography (CT). However, due to the changed X-ray spectrum with the use of tin filters, disease diagnosis could be affected because it appears as images of different impressions from previous images. Therefore, this study evaluates the changes in images when using tin filter and high pitch in chest low-dose CT. In this study, images were acquired in groups of three for comparison. Group 1 did not apply to tin filter, and used the existing pitch 0.8. Group 2 used a tin filter, pitch 0.8, Group 3 used a tin filter, and pitch 2.5. To compare the image quality, the natural image quality evaluator (NIQE) and the blind/referenceless image quality evaluator (BRISQUE) were used among the blind quality evaluation factors depended on a no-reference basis. As a result, the NIQE values were low in the order of Group 1, Group 3, and Group 2. BRISQUE values were low in the order of Group 3, Group 2 and Group 1. This study confirms the superiority of images of tin filter and high pitch techniques in chest low-dose CT, which is considered to be a fundamental study for acquiring accurate images of patients with difficult breathing control.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4652-4659
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• 2022

Among the detector materials available at room temperature, thallium bromide (TlBr), which has a relatively high atomic number and density, is widely used for gamma camera imaging. This study aimed to verify the usefulness of TlBr through quantitative evaluation by modeling detectors of various compound types using Monte Carlo simulations. The Geant4 application for tomographic emission was used for simulation, and detectors based on cadmium zinc telluride and cadmium telluride materials were selected as a comparison group. A pixel-matched parallel-hole collimator with proven excellent performance was modeled, and phantoms used for quality control in nuclear medicine were used. The signal-to-noise ratio (SNR), contrast to noise ratio (CNR), sensitivity, and full width at half maximum (FWHM) were used for quantitative analysis to evaluate the image quality. The SNR, CNR, sensitivity, and FWHM for the TlBr detector material were approximately 1.05, 1.04, 1.41, and 1.02 times, respectively, higher than those of the other detector materials. The SNR, CNR and sensitivity increased with increasing detector thickness, but the spatial resolution in terms of FWHM decreased. Thus, we demonstrated the feasibility and possibility of using the TlBr detector material in comparison with commercial detector materials.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.19-28
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• 2007

The purpose of this paper is to present a new measure for fingerprint image quality assessment that has a considerable effect on evaluation of fingerprint databases. This paper introduces a hybrid segmentation method for measuring an image quality and evaluates the experimental results using various fingerprint databases. This study compares the performance of the proposed hybrid segmentation using variance and coherence of fingerprints against the NIST's NFIQ program. Although NFIQ is a most widely used tool, it classifies the image quality into 5 levels. However, the proposed hybrid method is developed to be conformant to the ISO standards and accordant to human visual perception. The experimental results demonstrate that the hybrid method is able to produce finer quality measures.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.