• The Korean Journal of Nuclear Medicine
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• v.35 no.5
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• pp.301-312
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• 2001

Purpose: Stomach cancer is one of the most common malignancies in Korea, but there is no report on FDG PET in patients with stomach cancer. We observed findings of FDG PET in patients with stomach cancer. Materials and Methods: In 13 patients with pre-operative stomach cancer, PET and CT were performed. Primary lesion and regional lymph nodes detection were aualyzed. Correlation between FDG uptake ratio and each prognostic factor of primary lesion was analyzed. In 19 patients diagnosed as recurrence or displaying suspicious symptoms, conventional work up including tumor marker and PET were performed. Recurrence detection of anastomotic site, distant metastasis, and tumor marker elevation were analyzed. Results: Sensitivity for primary lesion detection was 83.3% (CT 71.4%) and two submucosal lesions were undetected. FDG uptake ratio was variable and had no correlation with invasion-depth, size, Borrmann type, staging and differentiation. Sensitivity for regional lymph node detection was 58.3% (CT 58.3%) and the lesions less than 1cm were undetected. Sensitivity for recurrence detection was 100% but there were three false positives. Sensitivity for distant metastasis detection was 64.3% and significantly higher than that of conventional work-up (21.4%). Average of tumor marker level in patients who were confirmed as recurrence was higher than false positive. Conclusion: PET is more useful than conventional work up in distant metastasis detection when recurrence is suspected. In pre-operative stomach cancer, PET is comparable to CT for detection of primary lesion and regional lymph node metastasis and detection of distant metastasis requires further study.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.209-220
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• 2012

In this paper, we propose an adaptive pavement region detection method that is robust to changes of structural patterns in a natural scene. In order to segment out a pavement reliably, we propose two step approaches. We first detect the borderline of a pavement and separate out the candidate region of a pavement using VRays. The VRays are straight lines starting from a vanishing point. They split out the candidate region that includes the pavement in a radial shape. Once the candidate region is found, we next employ the adaptive multi-seed region growing(A-MSRG) method within the candidate region. The A-MSRG method segments out the pavement region very accurately by growing seed regions. The number of seed regions are to be determined adaptively depending on the encountered situation. We prove the effectiveness of our approach by comparing its performance against the performances of seed region growing(SRG) approach and multi-seed region growing(MSRG) approach in terms of the false detection rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.9-18
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• 2015

In recent years, the number of applications embedded in the various devices such as a smart phone is getting larger. Due to the frequent changes of states in the execution environment, various malfunctions may occur. In order to handle the issue, this paper suggests an approach to detecting method-level failures in the legacy software systems. We can determine if the software executes the abnormal behavior based on the behavior model. However, when we apply the context-sensitive behavior model to the method-level, several problems happen such as false alarms and monitoring overhead. To tackle those issues, we propose CIBFD (Context-Insensitive Behavior Model-based Failure Detection) method. Through the case studies, we compare CIBFD method with the existing method. In addition, we analyze the effectiveness of the method for each application domains.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.181-188
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• 2011

With the rapid proliferation of wireless networks and mobile computing applications, the landscape of the network security has greatly changed recently. Especially, Vehicular Ad Hoc Networks maintaining network topology with vehicle nodes of high mobility are self-organizing Peer-to-Peer networks that typically have short-lasting and unstable communication links. VANETs are formed with neither fixed infrastructure, centralized administration, nor dedicated routing equipment, and vehicle nodes are moving, joining and leaving the network with very high speed over time. So, VANET-security is very vulnerable for the intrusion of malicious and misbehaving nodes in the network, since VANETs are mostly open networks, allowing everyone connection without centralized control. In this paper, we propose a weighted intrusion detection method using rough set that can identify malicious behavior of vehicle node's activity and detect intrusions efficiently in VANETs. The performance of the proposed scheme is evaluated by a simulation study in terms of intrusion detection rate and false alarm rate for the threshold of deviation number ${\epsilon}$.

• The Korean Journal of Pain
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• v.28 no.2
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• pp.105-108
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• 2015

Background: The most definitive diagnosis of neck pain caused by facet joints can be obtained through cervical medial branch blocks (CMBBs). However, intravascular injections need to be carefully monitored, as they can increase the risk of false-negative blocks when diagnosing cervical facet joint syndrome. In addition, intravascular injections can cause neurologic deficits such as spinal infarction or cerebral infarction. Digital subtraction angiography (DSA) is a radiological technique that can be used to clearly visualize the blood vessels from surrounding bones or dense soft tissues. The purpose of this study was to compare the rate of detection of intravascular injections during CMBBs using DSA and static images obtained through conventional fluoroscopy. Methods: Seventy-two patients were included, and a total of 178 CMBBs were performed. The respective incidences of intravascular injections during CMBBs using DSA and static images from conventional fluoroscopy were measured. Results: A total of 178 CMBBs were performed on 72 patients. All cases of intravascular injections evidenced by the static images were detected by the DSAs. The detection rate of intravascular injections was higher from DSA images than from static images (10.7% vs. 1.7%, P < 0.001). Conclusions: According to these findings, the use of DSA can improve the detection rate of intravascular injections during CMBBs. The use of DSA may therefore lead to an increase in the diagnostic and therapeutic value of CMBBs. In addition, it can decrease the incidence of potential side effects during CMBBs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2785-2791
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• 2015

In this paper, neddle detection algorithm for the removal of needle stuck into skin in oriental clinic is presented. First, in the proposed method, potential candidate areas of each needle are selected by using the morphological open operation in a gray image, and the false candidates are removed by considering their area size. Next, edge points are extracted using canny edge detector in selected candidate areas, line segments are approximated using the edge points. Based on the direction of line segment and the distance between two line segments, two main line segments of the needle are extracted. The final verification of needle is accomplished by using the morphological analysis of these two line segments. In the experiments, the detection rate of proposed method reaches to 97.5% for the 16 images containing 119 needles.

• Journal of KIISE:Information Networking
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• v.34 no.3
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• pp.186-192
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• 2007

The virus throttling technique[5,6] is the one of well-known worm early technique. Virus throttling reduce the worm propagration by delaying connection packets artificially. However the worm detection time is not sufficiently fast as expected when the worm generated worm packets at a low rate. This is because the virus throttling technique use only delay queue length. In this paper we use the trend of weighted average delay queue length (TW AQL). By using TW AQL, the worm detection time is not only shorten at a low rate Internet worm, but also the false alarm does not largely increase. By experiment, we also proved our proposed algorithm had better performance.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1128-1141
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• 2010

This paper proposes a method of moving object detection in crowded scene using clustered trajectory. Unlike previous appearance based approaches, the proposed method employes motion information only to isolate moving objects. In the proposed method, feature points are extracted from input frames first and then feature tracking is followed to create feature trajectories. Based on an assumption that feature points originated from the same objects shows similar motion as the object moves, the proposed method detects moving objects by clustering trajectories of similar motions. For this purpose an energy function based on spatial proximity, motion coherence, and temporal continuity is defined to measure the similarity between two trajectories and the clustering is achieved by minimizing the energy function in CRFs (conditional random fields). Compared to previous methods, which are unable to separate falsely merged trajectories during the clustering process, the proposed method is able to rearrange the falsely merged trajectories during iteration because the clustering is solved my energy minimization in CRFs. Experiment results with three different crowded scenes show about 94% detection rate with 7% false alarm rate.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.