• Korean Journal of Radiology
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• v.22 no.11
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• pp.1797-1808
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• 2021

Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion: Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.155-162
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• 2012

Devices to measure the blood pressure of patients are being used without any calibration in a hospital. It is an important to show consistent values when any medical devices measure the same patients regardless they are sphygmomanometer or fully automatic electronic blood pressure meter. We compared sphygmomanometer and fully automatic electronic blood pressure meters with standard digital blood pressure monitor (SDBPM) to evaluate the consistency of the small healthy subjects. We measured the blood pressure from six healthy subjects (three of 20~40 years and three of 40~60 years old). Two sphygmomanometer and two fully automatic electronic blood pressure meters were used and compared with the SDBPM. Blood pressures measured from right and left arms each and were compared. All six healthy subjects showed normal blood pressure values. In general, left blood pressure values showed higher values than right side. Comparing SDBPM, with the other monitors, the systolic pressure showed ${\pm}$ 34.8% difference and ${\pm}$ 33.3% for the diastolic pressure. Correlation between SDBPM and Sphygmomanometer was 0.59~0.71, and 0.50~0.70 for fully automated digital BP monitors. It fell in grade-D when we apply the BHS(British hypertension society). AAMI(American association for the advancement of medical instrumentation) also showed unsatisfactory results for the mean value (${\leq}$ 5 mmHg) and standard deviation (${\leq}$ 8 mmHg). We tested sphygmomanometer and fully automatic electronic blood pressure meters and compared with a standard digital blood pressure monitor. All devices showed inconsistent blood pressures. A reliable calibration system is highly needed for all devices in all hospitals.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.67-74
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• 2019

Visual inspection is regarded as one of the critical activities for quality control in a manufacturing company. it is thus important to improve the performance of detecting a defective part or product. There are three probable working modes for visual inspection: fully automatic (by automatic machines), fully manual (by human operators), and semi-automatic (by collaboration between human operators and automatic machines). Most of the current studies on visual inspection have been focused on the improvement of automatic detection performance by developing a better automatic machine using computer vision technologies. However, there are still a range of situations where human operators should conduct visual inspection with/without automatic machines. In this situation, human operators'performance of visual inspection is significant to the successful quality control. However, visual inspection of components assembled into a mobile camera module belongs to those situations. This study aims to investigate human performance issues in visual inspection of the components, paying more attention to human errors. For this, Abstraction Hierarchy-based work domain modeling method was applied to examine a range of direct or indirect factors related to human errors and their relationships in the visual inspection of the components. Although this study was conducted in the context of manufacturing mobile camera modules, the proposed method would be easily generalized into other industries.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1215-1230
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• 1995

The purpose of this paper is to build a computer vision system that endows an autonomous mobile robot the ability of automatic measuring of the analog and digital meters installed in nuclear power plant(NPP). This computer vision system takes a significant part in the organization of automatic surveillance and measurement system having the instruments and gadzets in NPP under automatic control situation. In the meter image captured by the camera, the meter area is sorted out using mainly the thresholding and the region labeling and the meter value recognition process follows. The positions and the angles of the needles in analog meter images are detected using the projection based method. In the case of digital meters, digits and points are extracted and finally recognized through the neural network classifier. To use available database containing relevant information about meters and to build fully automatic meter recognition system, the segmentation and recognition of the function-name in the meter printed around the meter area should be achieved for enhancing identification reliability. For thus, the function- name of the meter needs to be identified and furthermore the scale distributions and values are also required to be analyzed for building the more sophisticated system and making the meter recognition fully automatic.

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.300-302
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• 2001

This paper describes several advances in vision sensor and process control techniques for applications in Submerged Arc Welding(SAW) which combine to give a fully automatic system capable of controlling and adapting the overall welding process.

• Progress in Medical Physics
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• v.15 no.2
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• pp.70-76
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• 2004

The most important work for early detection of liver cancer and decision of its characteristic and location is good segmentation of a liver region from other abdominal organs. This paper proposes a fully automatic liver segmentation algorithm based on the abdominal morphology characteristic as an easy and efficient method. Multi-modal threshold as pre-processing is peformed and a spine is segmented for finding morphological coordinates of an abdomen. Then the liver region is extracted using C-class maximum a posteriori (MAP) decision and morphological filtering. In order to estimate results of the automatic segmented liver region, area error rate (AER) and correlation coefficients of rotational binary region projection matching (RBRPM) are utilized. Experimental results showed automatic liver segmentation obtained by the proposed algorithm provided strong similarity to manual liver segmentation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.147-154
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• 2017

Purpose: To reduce the costs of potato seeds and labor of workers, a fully automatic in-situ seeding machine for cutting seed potatoes was developed. Methods: An experiment was conducted to evaluate the seeder performance of the prototype of potato planter by cutting seeds in farmlands from March to April 2017. The study tested the seeder performance at working speeds ranging from 0.28 to 0.45 m/s. The seeding rate and seeding distance were also investigated according to the planned distance between planted seeds from 20 to 30 cm, with 5 cm intervals. Results: Tests on the performance of the developed cutting blade on the automatic potato seeder show that whole potatoes should be used instead of half potatoes. The seeding rates were 88.8% and 82.5% for whole and half potatoes, respectively. When the tractor working speed was increased from 0.28 to 0.45 m/s, the successful seeding rate decreased from 98.8% to 96.3%, respectively. However, with planted seed distances of 20, 25, and 30 cm, the successful seeding rates were near 98%. Conclusions: The developed automatic potato seeder can to improve the labor productivity and cultivation environment of potato farms by the mechanization of the seeding process, which is currently associated with high-labor, -costs, and -hours. Therefore, based on this study, the developed automatic potato seeder provides the mechanization necessary for improved potato cultivation conditions in farmlands.

• Journal of Astronomy and Space Sciences
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• v.17 no.2
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• pp.211-220
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• 2000

Korea Astronomy Observatory(KAO) is working to retrofit its 1m robotic telescope in collaboration with a company (ACE, Astronomical Consultants & Equipment). The telescope system is being totally refurbished to make a fully automatic telescope which can operate in both interactive and fully autonomous robotic modes. Progress has been made in design and manufacturing of the telescope mount, mechanics, and optical performance system tests are being made for re-configured primary and secondary mirrors. The optical system is designed to collect 80% incident light within 0.5 arcsec with f/7.5 Ritchey-Chretien design. The telescope mount is an equatorial fork with a friction drive system. The design allows fully programmable tracking speeds with typical range of 15 arcsec/sec with accuracy of $\pm5$ arcsec/hour. The mount system has integral pointing model software to correct for refraction, and all mechanical errors and misalignments. The pointing model will permit positioning to better than 30 arcsec RMS within $75^{\circ}$ from zenith and 45 arcsec RMS elsewhere on the sky. The software is designed for interactive, remote and robotic modes of operation. In interactive and remote mode the user can manually enter coordinates or retrieve them from a computer file. In robotic mode the telescope controller downloads the coordinates in the order determined by the scheduler. The telescope will be equipped with a CCD camera and will be accessible via the internet.