• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.193-198
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• 2022

Contrast enhanced magnetic resonance imaging using gadolinium-based contrast agent (GBCA) is a very useful in vivo technique to visualize the inner ear pathology including endolymphatic hydrops. Although systemic intravenous (IV) administration can visualize the perilymph space, the visualization was possible by indirect passage of contrast agent through blood-perilymph barrier. All animal experimental procedures were performed under anesthesia with 5% isoflurane. Lipopolysaccharide (LPS) was instilled into the left tympanic cavity through the tympanic membrane using a sterile 27gauge needle to induce hydrops model. Tucker-Davis Technologies system was used to measure Auditory Brainstem Responses (ABRs). For intracerebroven-tricular (ICV) administration, 25 µmol of GADOVIST (Bayer, Berlin, Germany) was used and diluted GADOVIST injection was 10 µl. MR imaging was acquired with a 9.4 Tesla MRI scanner. Transmit-receive volume coil with 40 mm inner diameter and 75 mm out diameter was used. ICV administration well demonstrated the strong enhancement along the cerebrospinal fluid (CSF) microcirculation pathway including CSF fluid in the subarachnoid space and CSF space of the inner ear structures. On the other hand, IV administration showed no contrast enhancement along the CSF microcirculation pathway and showed weak enhancement in the inner ear structures. In case of rat hydrops model, ICV administration showed that the reduced contrast enhancement in the perilymph space of the hydrops induced inner ear compared to the contrast enhancement in the perilymph space of the normal inner ear. New systemic ICV administration method provide contrast enhancement of GBCA in the inner ear through CSF microcirculation pathway.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2315-2321
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• 2013

This study attempt to develope and suggest a new, minimize side effects process for calculate a time to peak enhancement of contrast level by using blood flow instead of current mathematical process. We conducted a studies 127 patients who performed the CE MRA by using test-contrast inject way. We used measurements of a contrast inflow time and time to peak enhancement of contrast level of each cerebrovascular branch for similarity of witch cerebrovascular branch calculate a time to peak enhancement of contrast level by using blood flow in image compared with calculation a time to peak enhancement of contrast level by using current mathematical process after contrast enhancement. In this study, confidence interval were used if the variable is continuous variable; there is differences between 4 groups exist but in group 1, there is no difference with time in peak enhancement of contrast level by using mathematical method to inflow time in sinus sigmoideus. it was significant statistically, in addition there was significant low heterogeneity in Bland Altman plot. Thus, apply a new calculate a time to peak enhancement of contrast level by using blood flow method will minimize damage caused by side effect, maintain quality of image, easy and fast access. It should provide a space for the exchange of current calculate a time to peak enhancement of contrast level by using mathematical process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.295-299
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• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• Korean Journal of Radiology
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• v.1 no.2
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• pp.91-97
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• 2000

Objective: To determine whether the time-intensity curves acquired by test and main dose contrast injections for MR angiography are similar. Materials and Methods: In 11 patients, repeated contrast-enhanced 2D-turbo-FLASH scans with 1-sec interval were obtained. Both test and main dose timeintensity curves were acquired from the abdominal aorta, and the parameters of time-intensity curves for the test and main boluses were compared. The parameters used were arterial and venous enhancement times, arterial peak enhancement time, arteriovenous circulation time, enhancement duration and enhancement expansion ratio. Results: Between the main and test boluses, arterial and venous enhancement times and arteriovenous circulation time showed statistically significant correlation (p < 0.01), with correlation coefficients of 0.95, 0.92 and 0.98 respectively. Although the enhancement duration was definitely greater than infusion time, reasonable measurement of the end enhancement point in the main bolus was impossible. Conclusion: Only arterial and venous enhancement times and arteriovenous circulation time of the main bolus could be predicted from the test-bolus results. The use of these reliable parameters would lead to improvements in the scan timing method for MR angiography.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.913-914
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• 2008

This paper presents a method of adaptive image enhancement with dynamic range compression and contrast enhancement. The dynamic range compression is to adaptively enhance the dark area using illumination component of DCT compression block. The contrast enhancement is to modify the image contrast using retinex theory that uses the HVS properties. The block artifacts and other noises, caused by processing in the compression domain, were removed by after processing.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.89-92
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• 2017

This paper investigates simple gray level image enhancement technique based on Genetic Algorithms and Local Statistics. The task of GA is to adapt the parameters of local sliding masks over pixels, finding out the best parameters preserving the brightness and possibly preventing the creation of intensity artifacts in the local area of images. The algorithm is controlled by GA as to enhance the contrast and details in the images automatically according to an object fitness criterion. Results obtained in terms of subjective and objective evaluations, show the plausibility of the method suggested here.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.741-744
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• 2008

In this paper, tone mapping method by local contrast and detail enhancement for High Dynamic Range (HDR) is proposed. By applying Piecewise Dynamic Range Histogram Adjustment (PDRHA) and Detail Enhancement Volume (DEV) with decomposed layers, tone mapping is performed effectively. The experimental results show that the proposed method preserves local contrast and overall impression with naturalness of original images.

• Journal of Medicine and Life Science
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• v.16 no.1
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• pp.1-5
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• 2019

Our purpose was to evaluate usefulness of the contrast-enhanced 3 dimensional fluid attenuated inversion recovery (3D-FLAIR) technique of half brain volume to diagnose the patients with facial neuritis based on segment-based analysis. We assessed retrospectively 17 consecutive patients who underwent brain MR imaging at 3 tesla for facial neuritis: 11 patients with idiopathic facial neuritis and 6 with herpes zoster oticus. Contrast enhanced 3D-FLAIR sequences of the half brain volume were analyzed and 3D T1-weighted sequence of the full brain volume were used as the base-line exam. Enhancement of the facial nerve was determined in each segment of 5 facial nerve segments by two radiologists. Sensitivity, specificity and accuracy of enhancement of each segment were assessed. The authors experienced a prompt fuzzy CSF enhancement in the fundus of the internal auditory canal in patients with enhancement of the canalicular segment. Interobserver agreement of CE 3D-FLAIR was excellent(${\kappa}$-value 0.885). Sensitivity, specificity, and accuracy of each segment are 1.0, 0.823, 0.912 in the canalicular segment; 0.118, 1.0, 0.559 in the labyrinthine segment; 0.823, 0.294, 0.559 in the anterior genu; 0.823, 0.529, 0.676 in the tympanic segment; 0.823, 0.235, 0.529 in the mastoid segment, respectively. In addition, those of prompt fuzzy enhancement were 0.647, 1.0, and 0.824, respectively. Incidence of prompt fuzzy enhancement with enhancement of the canalicular segment was 11 sites(55%): 6 (54.5%) in idiopathic facial neuritis and 5 (83.3%) in herpes zoster. Enhancement of the canalicular segment and prompt fuzzy enhancement on CE 3D-FLAIR was significantly correlated with occurrence of facial neuritis (p<0.001). CE 3D-FLAIR technique of the half brain volume is useful to evaluate the patients with facial neuritis as an adjunct sequence in addition to contrast-enhanced 3D T1-weighted sequence. On segment-based analysis, contrast enhancement of the canalicular segment is the most reliable. Prompt fuzzy enhancement is seen in not only herpes zoster, but in idiopathic facial neuritis.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.608-617
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• 2017

Methods of contrast enhancement have problem such as side effect of over-enhancement with non-gaussian histogram distribution, tradeoff enhancement efficiency against brightness preserving. In order to enhance contrast at various histogram distribution, segmentation to region with gaussian distribution and then enhance contrast each region. First, we segment an image into several regions using GMM(Gaussian Mixture Model)fitting by that k-mean clustering and EM(Expectation-Maximization) in $L^*a^*b^*$ color space. As a result region segmentation, we get the region map and probability map. Then we apply local contrast enhancement algorithm that mean shift to minimum overlapping of each region and preserve brightness histogram equalization. Experiment result show that proposed region based contrast enhancement method compare to the conventional method as AMBE(AbsoluteMean Brightness Error) and AE(Average Entropy), brightness is maintained and represented detail information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.837-856
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• 2016

Underwater image enhancement has received considerable attention in last decades, due to the nature of poor visibility and low contrast of underwater images. In this paper, we propose a new automatic underwater image enhancement algorithm, which combines nonsubsampled contourlet transform (NSCT) domain enhancement techniques with the mechanism of the human visual system (HVS). We apply the multiscale retinex algorithm based on the HVS into NSCT domain in order to eliminate the non-uniform illumination, and adopt the threshold denoising technique to suppress underwater noise. Our proposed algorithm incorporates the luminance masking and contrast masking characteristics of the HVS into NSCT domain to yield the new HVS-based NSCT. Moreover, we define two nonlinear mapping functions. The first one is used to manipulate the HVS-based NSCT contrast coefficients to enhance the edges. The second one is a gain function which modifies the lowpass subband coefficients to adjust the global dynamic range. As a result, our algorithm can achieve contrast enhancement, image denoising and edge sharpening automatically and simultaneously. Experimental results illustrate that our proposed algorithm has better enhancement performance than state-of-the-art algorithms both in subjective evaluation and quantitative assessment. In addition, our algorithm can automatically achieve underwater image enhancement without any parameter tuning.