• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1993.10a
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• pp.351-354
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• 1993

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.118-122
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• 1987

The medical image processing system is intended for a diverse set of users in the medical Imaging Parts. This system consists of a 640 Kbyte IBM-PC/AT with 30 Mbyte hard disk, special purpose image processor with video input devices and display monitor. Image may be recorded and processed in real time at sampling rate up to 10 MHz. This system provides a wide range of image enhancement processing facilities via a menu-driven software packages. These facilities include point by point processing, image averaging, convolution filter and subtraction.

• Investigative Magnetic Resonance Imaging
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• v.22 no.2
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• pp.102-109
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• 2018

Purpose: The purpose of this study is to compare the performance of the T1 3D subtraction technique and the conventional 2D dynamic contrast enhancement (DCE) technique in diagnosing Cushing's disease. Materials and Methods: Twelve patients with clinically and biochemically proven Cushing's disease were included in the study. In addition, 23 patients with a Rathke's cleft cyst (RCC) diagnosed on an MRI with normal pituitary hormone levels were included as a control, to prevent non-blinded positive results. Postcontrast T1 3D fast spin echo (FSE) images were acquired after DCE images in 3T MRI and image subtraction of pre- and postcontrast T1 3D FSE images were performed. Inter-observer agreement, interpretation time, multiobserver receiver operating characteristic (ROC), and net benefit analyses were performed to compare 2D DCE and T1 3D subtraction techniques. Results: Inter-observer agreement for a visual scale of contrast enhancement was poor in DCE (${\kappa}=0.57$) and good in T1 3D subtraction images (${\kappa}=0.75$). The time taken for determining contrast-enhancement in pituitary lesions was significantly shorter in the T1 3D subtraction images compared to the DCE sequence (P < 0.05). ROC values demonstrated increased reader confidence range with T1 3D subtraction images (95% confidence interval [CI]: 0.94-1.00) compared with DCE (95% CI: 0.70-0.92) (P < 0.01). The net benefit effect of T1 3D subtraction images over DCE was 0.34 (95% CI: 0.12-0.56). For Cushing's disease, both reviewers misclassified one case as a nonenhancing lesion on the DCE images, while no cases were misclassified on T1 3D subtraction images. Conclusion: The T1 3D subtraction technique shows superior performance for determining the presence of enhancement on pituitary lesions compared with conventional DCE techniques, which may aid in diagnosing Cushing's disease.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.63-66
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• 2009

Automatic segmentation of foreground from background in video sequences has attracted lots of attention in computer vision. This paper proposes a novel framework for the background subtraction that the foreground is segmented from the background by directly subtracting a background image from each frame. Most previous works focus on the extraction of more reliable seeds with threshold, because the errors are occurred by noise, weak color difference and so on. Our method has good segmentations from the approximate seeds by using the Random Walks with Restart (RWR). Experimental results with live videos demonstrate the relevance and accuracy of our algorithm.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1418-1426
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• 2009

There has been a lot of interest in an effective method for background subtraction in an effort to separate foreground objects from a predefined background image. Promising results on background subtraction using statistical methods have recently been reported are robust enough to operate in dynamic environments, but generally require very large computational resources and still have difficulty in obtaining clear segmentation of objects. We use a simple running-average method to model a gradually changing background, instead of using a complicated statistical technique. We employ a single global threshold vector, optimized by a genetic algorithm, instead of pixel-by-pixel thresholds. A new fitness function is defined and trained to evaluate segmentation result. The system has been implemented on a PC with a webcam, and experimental results on real images show that the new method outperforms an existing method based on a mixture of Gaussian.

• Imaging Science in Dentistry
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• v.46 no.1
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• pp.17-24
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• 2016

Purposes: This study employed a posteriori registration and subtraction of radiographic images to quantify the apical root resorption in maxillary permanent central incisors after orthodontic treatment, and assessed whether the external apical root resorption (EARR) was related to a range of parameters involved in the treatment. Materials and Methods: A sample of 79 patients (mean age, $13.5{\pm}2.2years$) with no history of trauma or endodontic treatment of the maxillary permanent central incisors was selected. Periapical radiographs taken before and after orthodontic treatment were digitized and imported to the Regeemy software. Based on an analysis of the post-treatment radiographs, the length of the incisors was measured using Image J software. The mean EARR was described in pixels and relative root resorption (%). The patient's age and gender, tooth extraction, use of elastics, and treatment duration were evaluated to identify possible correlations with EARR. Results: The mean EARR observed was $15.44{\pm}12.1pixels$ (5.1% resorption). No differences in the mean EARR were observed according to patient characteristics (gender, age) or treatment parameters (use of elastics, treatment duration). The only parameter that influenced the mean EARR of a patient was the need for tooth extraction. Conclusion: A posteriori registration and subtraction of periapical radiographs was a suitable method to quantify EARR after orthodontic treatment, and the need for tooth extraction increased the extent of root resorption after orthodontic treatment.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.159-161
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• 2004

Many motion object detection algorithms rely on the process of background subtraction, an important technique that is used for detecting changes from a model of the background scene. This paper propose a novel method to update the background model image of a visual surveillance system which is not stationary. In order to do this, we use a background model based on statistical qualities of monitored images and another background model that excluded motions. By comparing each changed area computed from the two background model images and current monitored image, the areas that will be updated are decided.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.623-628
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• 2008

We present a new method of extracting foreground object from background image for vision-based game interface. Background Subtraction is an important preprocessing step for extracting the features of tracking objects. The image is divided into the cells where the Local Histogram with Gaussian kernel is computed and compared with the corresponding one using Bhattacharyya distance measure. The histogram-based method is partially robust against illumination change, noise and small moving objects in background. We propose a Multi-Scaled Integral Histogram approach for noise suppression and fast computation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.17-22
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• 2013

In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.7
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.