• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.2
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• pp.277-285
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• 1997

The prupose of this study were to: (1) Analize the contents o( the cosmetics advertisements, (2) Compare the ideal woman images perceived by women with the women images profected through models used in the cosmetics advertisements, (3) Compare the ideal clothing images perceived by women with the clothing images profected through models used in the cosmetics advertisemtnts, and (4) Study and analise the purchase patterns of the women in theirtwenties. The subjects for this study were selected from students attending the Ewha Women's University. The study was done through video presentation, and questionnaire based on several previous studies, For the analysis of data, SPSS statistical packages were used. The results of emprical studies were summarized as follows: 1, As the result of the analysis of the current cosmetics advertisenents, the copies were mostly sentimental and short, the models mostly were casual dresses and their attitudes were active and natural for the most part. 2. The result of comparison between the images of women shows noticeable difference in that, the women tend to idealize independent intellectual and refined woman where as the models tend to be free, airy, outgoing and cute. 3. The ideal clothing images perceived by women tendto be intellectual, dignified, and neat, while the clothing worn by the models tend to be casual, cute, and sexy, thus showing noticeable discrepancy.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.32-40
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• 2022

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1641-1648
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• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• Journal of radiological science and technology
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• v.46 no.3
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• pp.207-217
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• 2023

Image noise reduction algorithm performs important functions in light microscopy. This study aims to systematically review the research trend of types and performance evaluation methods of noise reduction algorithm in light microscopic images. A systematic literature search of three databases of publications from January 1985 to May 2020 was conducted; of the 139 publications reviewed, 16 were included in this study. For each research result, the subjects were categorized into four major frameworks-1. noise reduction method, 2. imaging technique, 3. imaging type, and 4. evaluation method-and analyzed. Since 2003, related studies have been conducted and published, and the number of papers has increased over the years and begun to decrease since 2016. The most commonly used method of noise reduction algorithm for light microscopy images was wavelet-transform-based technology, which was mostly applied in basic systems. In addition, research on the real experimental image was performed more actively than on the simulation condition, with the main case being to use the comparison parameter as an evaluation method. This systematic review is expected to be extremely useful in the future method of numerically analyzing the noise reduction efficiency of light microscopy images.

• The Journal of the Korea Contents Association
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• v.3 no.3
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• pp.85-97
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• 2003

Medical tomography images like CT, MRI, PET, SPECT, fMRI, ett have been widely used for diagnosis and treatment of a patient and for clinical study in hospital. In many cases, tomography images are scanned in several different modalities or with time intervals for a single subject for extracting complementary information and comparing one another. 3D image registration is mapping two sets of images for comparison onto common 3D coordinate space, and may be categorized to marker -based matching and feature-based matching. 3D registration of brain images has an important role for visual and quantitative analysis in localization of treatment area of a brain, brain functional research, brain mapping research, and so on. In this article, marker-based and feature-based matching methods which are often used are introduced.

• International Journal of Internet, Broadcasting and Communication
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• v.6 no.2
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• pp.13-16
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• 2014

This study aims to examine the effects of watching 2D and 3D images on the blink rate. Regarding the image watch, their blink rate for 2D and 3D images was separately checked for 1 minute in the 1m distance, before the watch, after 15 minutes of watch, and after 30 minutes of watch. About the change of their blink rate in the 2D image watch, it tended to become higher than that before watching the image; however, there was no statistical significance (paired t-test, p=0.106, p=0.062). And in the 2D image watch, it tended to increase in comparison between after 15 minutes and after 30 minutes, but there was no statistical significance (paired t-test, p=0.623). Meanwhile, about the change of their blink rate in the 3D image watch, it tended to decrease statistically significantly both after 15 minutes and after 30 minutes when compared with that before watching the image (paired t-test, p=0.000, p=0.000). In the 3D image watch, it tended to increase in comparison between after 15 minutes and after 30 minutes; however, there was no statistical significance (paired t-test, p=0.867).

• Textile Coloration and Finishing
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• v.26 no.1
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• pp.39-44
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• 2014

To confirm the performance and benefit of the developed online E-commerce Real Industrial Color(RIC) device, cotton and polyester were dyed with selected 39 colors. The captured images of dyed cotton and polyester by using RIC device were compared with original samples and confirmed ${\Delta}E$ using a spectrophotometer and RIC device. Overall, visual comparison of the captured images was similar to the real dyed samples. In high concentration of dyeings, the color consistency between real samples and captured images was better than in lower color concentration of dyeings. Similarly, the result was almost the same when the developed RIC device was used since ${\Delta}E$ values of RIC device were smaller compared with spectrophotometer. In this regards, the RIC device developed up to date can be assumed that it is more influenced by the color rather than fabric materials.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.13-24
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• 2016

This paper presents a method to evaluate the performance of subpixel localization operators using target image data. Subpixel localization of edges is important to extract the precise shape of objects from images. In this study, each target image was designed to provide reference lines and edges to which the localization operators can be applied. We selected two types of moment-based operators: Gray-level Moment (GM) operator and Spatial Moment (SM) operator for comparison. The original edge localization operators with kernel size 5 are tested and their extended versions with kernel size 7 are also tested. Target images were collected with varying Camera-to-Object Distance (COD). From the target images, reference lines are estimated and edge profiles along the estimated reference lines are accumulated. Then, evaluation of the performance of edge localization operators was performed by comparing the locations calculated by each operator and by superimposing them on edge profiles. Also, enhancement of edge localization by increasing the kernel size was also quantified. The experimental result shows that the SM operator whose kernel size is 7 provides higher accuracy than other operators implemented in this study.