• Journal of radiological science and technology
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• v.27 no.2
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• pp.7-12
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• 2004

MDCT is a useful, non-invasive, diagnostic tool in the evaluation of coronary artery disease. However, the image quality is affected by an irregular heart rhythm of the patients. Especially, premature ventricular contraction induced stair-step artifacts in the reconstruction of 2-D or 3-D images of the heart including coronary arteries. In recent, we experienced some improving of the image quality after correcting the PVC. Accordingly, the purpose of our study was to evaluate the effectiveness of the arrhythmia correction method, which was commercially available software, in improving the quality of the reconstruction images of the heart. Image analysis was performed, in consensus, by two radiologists. The scores for image quality were ranked as follows; excellent is 4 (image quality is markedly improved and is helpful in the image evaluation), good is 3 (image quality is mildly improved, but is somewhat helpful in the image evaluation), fair is 2 (image quality is improved and is not helpful in the image evaluation), and poor is 1 (image quality is not improved). We used ANOVA method to evaluate the statistical significant differences in the image qualities among the correction methods of the arrhythmia with below 0.05 of p-value. The method of moving the R-R interval showed statistically significant differences in improving of the image quality in patients with arrhythmia. We concluded that the regulation of R-R interval in patients with arrhythmia was an effective method to improve the image quality in the reconstructions of the MDCT coronary angiograms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.1-8
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• 2010

Some methods employing the Active Contour Model have been widely used to extract face contour. Their performance, however, depends on the initial position of the model and the coefficients of the energy function which should be reconsidered whenever illumination and environmental condition of an input image is changed. Additionally, the number of points in the contour model should increase drastically in order to extract a fine contour. In this paper, we thus propose a novel approach which extracts face contour by segmenting the face region with threshold values obtained by a histogram analysis technique in the separated region of input image. The proposed method shows good performance under various illumination and environmental condition since it extracts face contour by considering the characteristics of the input image.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.9 no.1
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• pp.3-8
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• 2023

Parkinson's disease is a neurodegenerative disease caused by damage to brain neurons related to dopamine. Non-clinical animal models mainly used in Parkinson's disease research include drug-induced models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and 6-hydroxydopamine, and genetically modified transgenic animal models. Parkinson's diagnosis can be made using brain imaging of the substantia nigra-striatal dopamine system and using a radiotracer that specifically binds to the dopamine transporter. In this study, ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane was used to confirm the image evaluation cutoff between normal and parkinson's disease models, and to confirm model persistence over time. In addition, the efficacy of single or combined administration of clinically used therapeutic drugs in parkinson's animal models was evaluated. Image analysis was performed using the PMOD software. Converted to standardized uptake value, and analyzed by standardized uptake value ratio by dividing the average value of left striatum by the average value of right striatum obtained by applying positron emission tomography images to the atlas magnetic resonance template. The image cutoff of the normal and the parkinson's disease model was calculated as SUVR=0.829, and it was confirmed that it was maintained during the test period. In the three-drug combination administration group, the right and left striatum showed a high symmetry of more than 0.942 on average and recovered significantly. Images using ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane are thought to be able to diagnose and evaluate treatment efficacy of non-clinical Parkinson's disease.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.663-669
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• 2017

In diagnostic medical imaging, it is essential to reduce the scattered radiation for the high medical image quality and low patient dose. Therefore, in this study, the influence of the scattered radiation on medical images was analyzed as the tube voltage increases. For this purpose, ANSI chest phantom was used to measure the scattering ratio, and the scattering effect on the image quality was investigated by RMS evaluation, RSD and NPS analysis. It was found that the scattering ratio with increasing x-ray tube voltage gradually increased to 48.8% at 73 kV tube voltage and to 80.1% at 93 kV tube voltage. As a result of RMS analysis for evaluating the image quality, RMS value according to increase of tube voltage was increased, resulting in low image quality. Also, the NPS value at 2.5 lp/mm spatial frequency was increased by 20% when the tube voltage was increased by 93 kV compared to the tube voltage of 73 kV. From this study, it can be seen that the scattering radiation have a significant effect on the image quality according to the increase of x-ray tube voltage. The results of this study can be used as basic data for the improvement of medical imaging quality.

• Journal of radiological science and technology
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• v.43 no.3
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• pp.195-208
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• 2020

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.35-41
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• 2017

In This Paper, We Propose the Development of a Medical Questionnaire Recognition System using Vision Technology. The Proposed System is Able to Accurately Recognize and Effectively Process a Large Number of Questionnaires used in Community Health Surveys in the Medical and Health Fields. The System Consists of Questionnaire Scanning, Answer Recognition and Error Data Processing, Result Data Verification, Image Storage and DB Construction, and Analysis of Questionnaire Results. Unlike Existing Systems, This System is Free from the Form of Questionnaires used, and Enables Accurate Recognition by Processing Various Markings and Erroneous Markings. Experimental Results Show that the Proposed System has 98.9% Recognition rate.

• Korean Journal of Radiology
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• v.23 no.5
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• pp.529-538
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• 2022

Objective: Since its introduction in 2011, the CT/MRI diagnostic Liver Imaging Reporting and Data System (LI-RADS) has been updated in 2014, 2017, and 2018. We evaluated the impact of CT/MRI diagnostic LI-RADS on liver MRI research methodology for the diagnosis of hepatocellular carcinoma (HCC). Materials and Methods: The MEDLINE, EMBASE, and Cochrane databases were searched for original articles reporting the diagnostic performance of liver MRI for HCC between 2011 and 2019. The MRI techniques, image analysis methods, and diagnostic criteria for HCC used in each study were investigated. The studies were classified into three groups according to the year of publication (2011-2013, 2014-2016, and 2017-2019). We compared the percentage of studies adopting MRI techniques recommended by LI-RADS, image analysis methods in accordance with the lexicon defined in LI-RADS, and diagnostic criteria endorsed by LI-RADS. We compared the pooled sensitivity and specificity between studies that used the LI-RADS and those that did not. Results: This systematic review included 179 studies. The percentages of studies using imaging techniques recommended by LI-RADS were 77.8% for 2011-2013, 85.7% for 2014-2016, and 84.2% for 2017-2019, with no significant difference (p = 0.951). After the introduction of LI-RADS, the percentages of studies following the LI-RADS lexicon were 0.0%, 18.4%, and 56.6% in the respective periods (p < 0.001), while the percentages of studies using the LI-RADS diagnostic imaging criteria were 0.0%, 22.9%, and 60.7%, respectively (p < 0.001). Studies that did not use the LI-RADS and those that used the LIRADS version 2018 showed no significant difference in sensitivity and specificity (86.3% vs. 77.7%, p = 0.102 and 91.4% vs. 89.9%, p = 0.770, respectively), with some difference in heterogeneity (I² = 94.3% vs. 86.7% in sensitivity and I² = 86.6% vs. 53.2% in specificity). Conclusion: LI-RADS imparted significant changes in the image analysis methods and diagnostic criteria used in liver MRI research for the diagnosis of HCC.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.153-154
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• 2008

Digital photographic analysis is currently considered as a routine procedure in clinic because periodic follow-up examinations can provide meaningful information for diagnosis. However, it is impractical to separately evaluate all suspicious lesions with conventional digital photographic systems, which have inconsistent characteristics of the environmental conditions. To address the issue, it is necessary for total diagnostic evaluation in clinic to integrate conventional systems. Previously, a multimodal digital photographic imaging system, which provides a conventional color image, parallel and cross polarization color images and a fluorescent color image, was developed for objective evaluation of facial skin lesions. Based on our previous study, we introduce a commercial product, "DermaVision-PRO," for routine use in clinical application in dermatology. We characterize the system and describe the image analysis methods for objective evaluation of skin lesions. In order to demonstrate the validity of the system in dermatology, sample images were obtained from subjects with various skin disorders, and image analysis methods were applied for objective evaluation of those lesions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.35-44
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• 2015

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.