• Journal of radiological science and technology
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• v.29 no.3
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• pp.125-132
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• 2006

The PET/CT scanner is an evolution in image technology. The two modalities are complementary with CT and PET images. The PET scan images are well known as low resolution anatomic landmak, but such problems may help with interpretation detailed anatomic framework such as that provided by CT scan. PET/CT offers some advantages-improved lesion localization and identification, more accurate tumor staging. etc. Conventional PET employs tranmission scan require around 4 min./bed position and 30 min. for whole body scan. But PET/CT scanner can reduced by 50% in whole body scan. Especially nowadays PET scanner LSO scintillator-based from BGO without septa and operate in 3-D acquisition mode with multidetectors CT. PET/CT scanner fusion problems solved through hardware rather than software. Such device provides with the capability to acquire accurately aligned anatomic and functional images from single scan. It is very important to effective detection from gamma ray source in PETdetector. And can be offer high quality diagnostic images. So we have study about detection processing of PET detector and high quality imaging process.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.

• Childhood Kidney Diseases
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• v.5 no.1
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• pp.22-29
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• 2001

Purpose : 99mTc DMSA renal scan have been widely used not only for tile evaluation of renal scars but also for the diagnosis of acute pyelonephritis. Recent studies have shown SPECT images have higher accuracy than the planar images with some controversy. We evaluated the availability of the SPECT images adding to planar images for the diagnosis of acute pyelonephritis(APN) and renal scar in children with urinary tract infection (UTI). Methods : 130 children with UTI (260 kidney units) and 22 follow-up children (44 kidney units) were included between January 1, 1997 and July 31, 1999 at Ewha University Mokdong Hospital. Planar Anterior and posterior images and SPECT axial and coronal images of 99mTc DMSA renal scan were obtained with Starcam 4000-i U.S.A. GE at 3 hours after 99mTc DMSA I.V. injection. The data were analyzed by Chi square test after Yates's correction. Results : The detection rate of the acute pyelonephritis by SPECT images was 12.3$\%$ higher than that of planar images ($47.7\%\;vs\;35.4\%$) by the patient and 6.9$\%$ higher also ($31.9\%\;vs\;25.4\%$) by the kidney unit. 18 kidney units with negative planar images had focal defect in 10 kidney units (3.8$\%$) and multifocal defect in 8 kidney units (3.1$\%$) on SPECT images, but 1 kidney unit with positive planar image had negative SPECT image. SPECT images were superior to tile planar images in 17.3$\%$. identical in 82.3$\%$ and inferior in 0.4$\%$ to planar image. The detection rate of tile renal scars by SPECT images was 13.7$\%$ higher than planar images by the patient ($68.2\%\;vs\;54.5\%$) and 6.8$\%$ higher also ($43.2\%\;vs\;36.4\%$) by the kidney unit. SPECT images were superior to the planar images in 17.3$\%$ and identical in 82.3$\%$ to planar image. Conclusion SPECT images had shown higher detection rate and better image than planar images for the diagnosis of the acute pyelonephritis and the evaluation of the renal scars. (J. Korean Soc Pediatr Nephrol 5 : 22- 9, 2001)

• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1477-1492
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• 2021

Purpose Dedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed. Materials and Methods Two disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student's t test. Results Total mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study. Conclusion The results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.85-97
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• 2001

The KT(Kauth-Thomas) and IHS(Intensity-Hue-Saturation) transformation techniques were introduced and compared to investigate fire-scarred areas with single post-fire Landsat 7 ETM+ image. This study consists of two parts. First, using only geometrically corrected imagery, it was examined whether or not the different level of fire-damaged areas could be detected by simple slicing method within the image enhanced by the IHS transform. As a result, since the spectral distribution of each class on each IHS component was overlaid, the simple slicing method did not seem appropriate for the delineation of the areas of the different level of fire severity. Second, the image rectified by both radiometrically and topographically was enhanced by the KT transformation and the IHS transformation, respectively. Then, the images were classified by the maximum likelihood method. The cross-validation was performed for the compensation of relatively small set of ground truth data. The results showed that KT transformation produced better accuracy than IHS transformation. In addition, the KT feature spaces and the spectral distribution of IHS components were analyzed on the graph. This study has shown that, as for the detection of the different level of fire severity, the KT transformation reflects the ground physical conditions better than the IHS transformation.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.30-38
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• 2017

In this paper, we propose an underwater acoustic sensor fault detection method for passive sonar systems. In general, a passive sonar system displays processed results of array signals obtained from tens of the acoustic sensors as a two-dimensional image such as displays for broadband or narrowband analysis. Since detection result display in the operation software is to display the accumulated result through the array signal processing, it is difficult to determine the possibility where signal may be contaminated by the fault or failure of a single channel sensor. In this paper, accordingly, we propose a detection method based on the analysis of RMSCR (Root Mean Square Crossing-Rate), and the processing techniques for the faulty sensors are analyzed. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured by using signals acquired from real array being operated in several coastal areas. Besides, we compare performance of fault processing techniques. From the experiments, it is shown that the proposed method works well in underwater environments with high average RMS, and mute (set to zero) shows the best performance with regard to fault processing techniques.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.201-209
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• 2000

Recent works regarding video shot change detection algorithms show that abrupt shot changes are detected fairly well. However, gradual shot changes including wipes and dissolves are often missed or falsely detected. A robust shot change detection system, therefore, must include a shot verification step to further enhance the overall system performance. In this paper, we introduce the concept of the visual rhythm which is a single image, a subsampled version of a full video. On the visual rhythm, the different video edit effects such as cuts, wipes and dissolves manifest themselves as different patterns. Using this characteristic, it becomes possible, without sequentially playing the entire video, to find false positive shots as well as undetected shots. Thus, inclusion of the visual rhythm in the shot boundary verification process will aid the operator to exclude falsely detected shots as well as to find undetected shots fast and efficiently. For this purpose we have developed a new tool, a shot verifier incorporating the visual rhythm. The usefulness of the visual rhythm during the shot verification process will be presented.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.65-71
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• 2014

This paper present the multi-modal user distance estimation system using mono camera and mono microphone basically equipped with a mobile device. In case of a distance estimation method using an image, we is estimated a distance of the user through the skin color region extraction step, a noise removal step, the face and eyes region detection step. On the other hand, in case of a distance estimation method using speech, we calculates the absolute difference between the value of the sample of speech input. The largest peak value of the calculated difference value is selected and samples before and after the peak are specified as the ROI(Region of Interest). The samples specified perform FFT(Fast Fourier Transform) and calculate the magnitude of the frequency domain. Magnitude obtained is compared with the distance model to calculate the likelihood. We is estimated user distance by adding with weights in the sorted value. The result of an experiment using the multi-modal method shows more improved measurement value than that of single modality.

• Imaging Science in Dentistry
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• v.34 no.4
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• pp.185-190
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• 2004

Purpose: To compare the diagnostic accuracy for the detection of root fractures in CMOS-based digital periapical images with conventional film-based periapical images. Materials and Methods: Sixty extracted single-root human teeth with closed apices were prepared endodontically and divided into two groups; artificially induced vertical root fracture group and control group. All radiographs were obtained using the paralleling technique. The radiographs were examined by 4 observers three times within a 4 week interval. Receiver operating characteristic (ROC) analysis was carried out using data obtained from four observers. Intra- and inter-examiner agreements were computed using kappa analysis. Results: The area under the ROC curve (Az) was used as an indicator of the diagnostic accuracy of the imaging system. Az values were as follows: direct-digital images; 0.93, film-based images; 0.92, and inverted digital images; 0.91. There was no significant difference between imaging modalities (P<0.05). The kappa value of inter-observer agreement was 0.42 (range: 0.28-0.60) and intra-observer agreement was 0.57 (range: 0.44-0.75). Conclusion : There is no statistical difference in diagnostic accuracy for the detection of vertical root fractures between digital periapical images and conventional periapical images. The results indicate that the CMOS sensor is a good image detector for the evaluation of vertical root fractures.