• 한국인공지능학회지
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• 제5권1호
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• pp.10-17
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• 2017

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

• 대한한의진단학회지
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• 제20권1호
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• pp.37-44
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• 2016

Ultrasonic imaging is the most widely modality among modern imaging device for medical diagnosis. Nevertheless, medical ultrasound images suffer from speckle noise and low contrast. In this paper, we propose probabilistic edge map for ultrasound image edge enhancement using automatic alien algorithm. The proposed method used applied speckle reduced ultrasound imaging for edge improvement using sequentially acquired ultrasound imaging. To evaluate the performance of method, the similarity between the reference and edge enhanced image was measured by quantity analysis. The experimental results show that the proposed method considerably improves the image quality with region edge enhancement.

• Journal of Acupuncture Research
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• 제31권1호
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• pp.75-93
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• 2014

Objectives : This study was designed to establish medical trust realization system by finding factors influential to it using questionnaire. Methods : 277 subjects were participated in this study. After a treatment, we conducted a survey from April 1st to October 31th about medical service perception index, medical trust index, patient satisfaction index, patient reliability index, patient flow degree index, recall intension index, and hospital image index. To evaluate the influence of medical service perception with other 6 indexes, we statistically made regression analysis of the results through the survey. Results : By the results of the analysis, evaluation of hospital image influenced all 6 indexes. The systemicity of treatment process had an effect on 5 indexes except for the flow degree of patients. The humanity of medical team brought out the estimation of 4 indexes except for the patient flow degree and hospital image. The empathic ability of doctor and appropriacy of medical costs hold the next rank influencing 3 indexes. It reached the conclusion that the systemicity of medical team tend to determine the medical trust and patient reliability. The expertise, professional skill of doctor, the fault, commercial application, fame of medical team, the speed of treatment process, the newest and clean medical facility affected each one index. Conclusions : Korean medicine should find a way to consider the mind of patients for improving the medical quality through trust realization system, keeping up with times. As a result of this research, we can find out important causes which influence the trustful medical system. From now on, we should apply this result to actual treatment of psychology customized system. Also, more simple and clear questionnaire was organized through this research, it can be used to forward research to apprehend patient mentality more conveniently.

• 대한세포병리학회지
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• 제19권2호
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• pp.136-143
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• 2008

Urothelial carcinoma accounts for 90% of all the cases of bladder cancer. Although many cases can be easily managed by local excision, urothelial carcinoma rather frequently recurs, tends to progress to muscle invasion, and requires regular follow-ups. Urine cytology is a main approach for the follow-up of bladder tumors. It is noninvasive, but it has low sensitivity of around 50% with using the conventional cytospin preparation. Liquid-based cytology (LBC) has been developed as a replacement for the conventional technique. We compared the cytomorphometric parameters of $ThinPrep^{(R)}$ and cytospin preparation urine cytology to see whether there are definite differences between the two methods and which technique allows malignant cells to be more effectively discriminated from benign cells. The nuclear-to-cytoplasmic ratio value, as measured by digital image analysis, was efficient for differentiating malignant and benign urothelial cells, and this was irrespective of the preparation method and the tumor grade. Neither the $ThinPrep^{(R)}$ nor the conventional preparation cytology was definitely superior for distinguishing malignant cells from benign cells by cytomorphometric analysis of the adequately preserved cells. However, the $ThinPrep^{(R)}$ preparation showed significant advantages when considering the better preservation and cellularity with a clear background.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 하계학술대회 논문집 Vol.8
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• pp.369-369
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• 2007

본 연구에서는 CD-RAD Phantom을 이용하여 의료영상의 명세성 (image clarity)분석을 위하여 동일한 X선 영상을 대상으로 물리적 평가와 시각적 평가를 비교분석 하였다. 측정방법은 CD-RAD Phantom을 X선 조사하여 CR 영상처리장치를 통해 영상을 획득하였으며, 영상분석은 CD-RAD analyser program을 통한 통계학적 방법으로 물리적 평가를 시행하고, 동일한 영상의 시각적 평가는 관찰자 20명을 대상으로 blind test를 시행하였다. 분석결과는 Contrast-detail curve의 물리적 평가 IQF값은 25, 시각적 평가 IQF값은 30으로 분석되어 물리적 평가가 시각적 평가에 비해 우수하게 나타났다. 의료영상의 특성은 영상 판독자에게 영상의 정보 전달능력이 매우 중요하므로 객관적인 물리적 분석법과 시각적 분석법이 병행되어야 한다고 판단된다.

• Korean Journal of Radiology
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• 제25권4호
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• pp.374-383
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• 2024

Objective: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learningbased image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE). Materials and Methods: This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols. Results: The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, P < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, P = 0.001), with improved depiction of hippocampal T2 high signal intensity change (P = 0.016) and loss of internal structure (P < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, P = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, P = 0.010), image quality, SNR, and CNR (all, P < 0.001). Conclusion: The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.

• Korean Journal of Radiology
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• 제22권10호
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• pp.1597-1608
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• 2021

Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.468-475
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• 2020

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

• 한국산업융합학회 논문집
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• 제27권3호
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• International Journal of Contents
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• 제5권4호
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• pp.7-12
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• 2009

In this paper, the authors showed that the removal of impulse noise in CR images was implemented using variety of median filters and SNR/PSNR measurements. They used three kinds of medical images-hand, skull, and knee- for experimental results. But the noise in CR image was only the impulse noise. In real medical image, the noise of an image would be very different type. Therefore. the lack of experimental results using different noise in CR images is one flaw.