• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2708-2714
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• 2009

Due to poor linguistic communication skills of sucklings and infants, crying mostly is only means of communication to express their body conditions and desires. We, therefore, developed an infant auscultation system which detects which part of the body has a pathological problem, by analysing infant's crying sound patterns. Specifically, in this paper, we accomplished an auscultation system for congenital heart diseases detection by performing pitch, intensity and spectrum analysis of the crying sounds between the normal infants group and the congenital heart diseases group. With this system, we can diagnose congenital heart diseases of infants with poor communication capacity, and, in the near future, can build a home care diagnosis system based on crying sound analysis technologies through additional experiments on medical data.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.90-97
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• 1999

Recently, there have been many researches to automate processing and analysing image data in medical field, due to the advance of image processing techniques, the fast communication network and high performance hardware. In this paper, we design and implement the system based on the multi-layer neural network model to be able to analyze, differentiate and count blood cells in the peripheral blood image. To do these, we segment red and white-blood cell in blood image acquired from microscope with CCD(Charge-coupled device) camera and then apply the various feature extraction algorithms to classify. In addition to, we reduce multi-variate feature number using PCA(Principle Component Analysis) to construct more efficient classifier. So, in this paper, we are sure that the proposed system can be applied to a pathological guided system.

• Journal of Gastric Cancer
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• v.19 no.2
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• pp.165-172
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• 2019

Purpose: The robotic system for surgery was introduced to gastric cancer surgery in the early 2000s to overcome the shortcomings of laparoscopic surgery. The more recently introduced da Vinci $Xi^{(R)}$ system offers benefits allowing four-quadrant access, greater range of motion, and easier docking through an overhead boom rotation with laser targeting. We aimed to identify whether the $Xi^{(R)}$ system provides actual advantages over the $Si^{(R)}$ system in gastrectomy for gastric cancer by comparing the operative outcomes. Materials and Methods: We retrospectively reviewed all patients who underwent robotic gastrectomy as treatment for gastric cancer from March 2016 to March 2017. Patients' demographic data, perioperative information, and operative and pathological outcomes were collected and analyzed. Results: A total of 109 patients were included in the $Xi^{(R)}$ group and 179 in the $Si^{(R)}$ group. Demographic characteristics were similar in both groups. The mean operative time was 229.9 minutes in the $Xi^{(R)}$ group and 223.7 minutes in the $Si^{(R)}$ group. The mean estimated blood loss was 72.7 mL in the $Xi^{(R)}$ group and 62.1 mL in the $Si^{(R)}$ group. No patient in the $Xi^{(R)}$ group was converted to open or laparoscopy, while 3 patients in the $Si^{(R)}$ group were converted, 2 to open surgery and 1 to laparoscopy, this difference was not statistically significant. Bowel function was resumed 3 days after surgery, while soft diet was initiated 4 days after surgery. Conclusions: We found no difference in surgical outcomes after robotic gastrectomy for gastric cancer between the da Vinci $Xi^{(R)}$ and da Vinci $Si^{(R)}$ procedures.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.1
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• pp.49-55
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• 2000

Recently, X-ray chest rediography is showing a tendency to take an image of digital radiography so as to diagnose the pathology of chest in a usual. When the radiologist observes the chest image derived from digital radiography system on the monitor, he feels difficult to find out the pathological pattern because the quality of chest radiography is unequal. It takes amount of time to adjust the proper image for diagnosis. Therefore, we propose the method of the chest image equalization using neural networks and provide the compared result with histogram equalization method.

• Journal of Korean Neurosurgical Society
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• v.53 no.2
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• pp.89-95
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• 2013

Objective : Similar to back pain, neck pain has recently shown to have increasing prevalence. Magnetic resonance imaging (MRI) is useful in identifying the causes of neck pain. However, MRI shows not only pathological lesions but also physiological changes at the same time, and there are few Korean data. The authors have attempted to analyze the prevalence of disc degeneration in highly selective asymptomatic Korean subjects using MRI. Methods : We performed 3 T MRI sagittal scans from C2 to T1 on 102 asymptomatic subjects (50 men and 52 women) who visited our hospital between the ages of 14 and 82 years (mean age 46.3 years). All images were read independently by three observers (two neurosurgeons and one neuroradiologist) who were not given any information about the subjects. We classified grading for cervical disc herniation (HN), annular fissure (AF), and nucleus degeneration (ND), using disc degeneration classification. Results : The prevalence of HN, AF, and ND were 81.0%, 85.9%, and 95.4%, respectively. High prevalence of HN, AF, and ND was shown compared to previous literature. Conclusion : In asymptomatic Korean subjects, the abnormal findings of 3 T MRI showed a high prevalence in HN, AF, and ND. Several factors might play important roles in these results, such as population-specific characters, MRI field strength, and disc degeneration grading system.

• Toxicological Research
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• v.13 no.4
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• pp.353-357
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• 1997

The possibility of the allergic encephalomyelitis caused by the combined vaccine (KGCC95VI) for the prophylaxis against Japanese encephalitis and Hantaan virus infection, recently developed by Korea Green Cross Corporation, was investigated in the Hartley guinea pigs. The KGCC-95VI was administered to the guinea pigs subcutaneously to sensitize the animals three times at one month intervals. There were no clinical signs or gross pathological findings. There were no abnormal histopathological findings at cerebrums, cerebellums, brain stems and the spinal cords. The concentration of myelin basic protein was 1.10 ng/dose quantified by ELISA, which met the guide4ine of below 2 ng/ml/dose recommended by American Society of Health -System Pharmacists(AHPS) Drug Information. Accordingly, the KGCC-95VI is considered not to induce any allergic immune responses which may lead to the experimental allergic encephalomyelitis.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• Journal of IKEEE
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• v.23 no.4
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• pp.1122-1127
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• 2019

In this study, the bio-impedance of the human body is able to obtain a lot of information by monitoring the pathological and physiological conditions of clinical and biological tissues. The four electrode method system for biometrics measured the potential difference between two electrodes and the other two electrodes were used as electrodes for current flow. The newly developed dry gold electrode measured impedance from 1 Hz to 50 kHz and produced reproducible results. To verify the impedance measurement of the dry electrode, the pitting was performed using an equivalent circuit model of the bioelectrode skin, and the effectiveness was demonstrated through modeling. Fixed electrode types have a constant position of the electrodes attached during the measurement, so that a stable measurement can be obtained, thereby minimizing the error.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2527-2530
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• 2015

Background: The cancer of the prostate risk assessment (CAPRA) score has been defined to predict prostate cancer recurrence based on the pre-clinical data, then pathological data have also been incorporated. Thus, CAPRA post-surgical (CAPRA-S) score has been developed based on six criteria (prostate specific antigen (PSA) at diagnosis, pathological Gleason score, and information on surgical margin, seminal vesicle invasion, extracapsular extension and lymph node involvement) for the prediction of post-surgical recurrences. In the present study, biochemical recurrence (BCR)-free probabilities after open retropubic radical prostatectomy (RP) were evaluated by the CAPRA-S scoring system and its three-risk level model. Materials and Methods: CAPRA-S scores (0-12) of our 240 radical prostatectomies performed between January 2000-May 2011 were calculated. Patients were distributed into CAPRA-S score groups and also into three-risk groups as low, intermediate and high. BCR-free probabilities were assessed and compared using Kaplan-Meier analysis and Cox proportional hazards regression. Ability of CAPRA-S in BCR detection was evaluated by concordance index (c-index). Results: BCR was present in 41 of total 240 patients (17.1%) and the mean follow-up time was $51.7{\pm}33.0$ months. Mean BCR-free survival time was 98.3 months (95% CI: 92.3-104.2). Of the patients in low, intermediate and high risk groups, 5.4%, 22.0% and 58.8% had BCR, respectively and the difference among the three groups was significant (P = 0.0001). C-indices of CAPRA-S score and three-risk groups for detecting BCR-free probabilities in 5-yr were 0.87 and 0.81, respectively. Conclusions: Both CAPRA-S score and its three-risk level model well predicted BCR after RP with high c-index levels in our center. Therefore, it is a clinically reliable post-operative risk stratifier and disease recurrence predictor for prostate cancer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2341-2346
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• 2017

The relative position of the eyeballs in the orbit can be a criterion for estimating multiple pathological conditions. Especially, it is useful to diagnose orbital fracture, thyroid eye disease, orbital tumor, and to evaluate the result of drug and surgical treatment. The Hertel and Naugle exophthalmometer, which are the most commonly used measuring instruments for measuring the prominence of the eye, are different from each other. Even if the same examiner repeatedly measures, it is inevitable. Also, even if the same exophthalmometer is different from the manufacturing company, the design of the fixed part of the orbit is different, and a measurement error is caused by the inspectors. In this paper, we propose a method of automatic measurement that can increase the accuracy and repeatability of measurement of exophthalmos using white light scanning interferometer, which is a 3D image measurement technique.