Automated Segmentation of the Lateral Ventricle Based on Graph Cuts Algorithm and Morphological Operations

Park, Seongbeom,Yoon, Uicheul The Korean Society of Medical and Biological Engin 2017 의공학회지 Vol.38 No.2

Enlargement of the lateral ventricles have been identified as a surrogate marker of neurological disorders. Quantitative measure of the lateral ventricle from MRI would enable earlier and more accurate clinical diagnosis in monitoring disease progression. Even though it requires an automated or semi-automated segmentation method for objective quantification, it is difficult to define lateral ventricles due to insufficient contrast and brightness of structural imaging. In this study, we proposed a fully automated lateral ventricle segmentation method based on a graph cuts algorithm combined with atlas-based segmentation and connected component labeling. Initially, initial seeds for graph cuts were defined by atlas-based segmentation (ATS). They were adjusted by partial volume images in order to provide accurate a priori information on graph cuts. A graph cuts algorithm is to finds a global minimum of energy with minimum cut/maximum flow algorithm function on graph. In addition, connected component labeling used to remove false ventricle regions. The proposed method was validated with the well-known tools using the dice similarity index, recall and precision values. The proposed method was significantly higher dice similarity index ($0.860{\pm}0.036$, p < 0.001) and recall ($0.833{\pm}0.037$, p < 0.001) compared with other tools. Therefore, the proposed method yielded a robust and reliable segmentation result.

영역분할과 컬러 특징을 이용한 건물 인식기법

허정훈,이민철 한국로봇학회 2013 로봇학회 논문지 Vol.8 No.2

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

Low-Complexity MIMO Detection Based on Belief Propagation Over Pairwise Graphs

Seokhyun Yoon,Chan-Byoung Chae IEEE 2014 IEEE Transactions on Vehicular Technology VT Vol.63 No.5

<P>This paper considers a belief propagation algorithm over pairwise graphical models to develop low-complexity iterative multiple-input multiple-output (MIMO) detectors. The pairwise graphical model is a bipartite graph where a pair of variable nodes are related by an observation node represented by the bivariate Gaussian function obtained by marginalizing the posterior joint probability density under the Gaussian input assumption. Specifically, we consider two types of pairwise models: the fully connected and ring-type. The pairwise graphs are sparse, compared with the conventional graphical model introduced by Bickson et al., insofar as the number of edges connected to an observation node (edge degree) is only two. Consequently, the computations are much easier than those of maximum likelihood (ML) detection, which are similar to the belief propagation (BP) that is run over the fully connected bipartite graph. The link level performance for non-Gaussian input is evaluated via simulations, and the results show the validity of the proposed algorithms. We also customize the algorithm with Gaussian input assumption to obtain the Gaussian BP run over the two pairwise graphical models, and for the ring-type, we prove its convergence to the linear minimum mean square error (MMSE) estimates. Since the maximum a posterior (MAP) estimator for Gaussian input is equivalent to the linear MMSE estimator, it shows the optimality of the scheme for Gaussian input.</P>

컴퓨터 게임 환경에서 일반화 가시성 그래프를 이용한 경로찾기

유견아,전현주,Yu, Kyeon-Ah,Jeon, Hyun-Joo 한국시뮬레이션학회 2005 한국시뮬레이션학회 논문지 Vol.14 No.3

In state-of-the-art games, characters can move in a goal-directed manner so that they can move to the goal position without colliding obstacles. Many path-finding methods have been proposed and implemented for these characters and most of them use the A* search algorithm. When .the map is represented with a regular grid of squares or a navigation mesh, it often takes a long time for the A* to search the state space because the number of cells used In the grid or the mesh increases for higher resolution. Moreover the A* search on the grid often causes a zigzag effect, which is not optimal and realistic. In this paper we propose to use visibility graphs to improve the search time by reducing the search space and to find the optimal path. We also propose a method of taking into account the size of moving characters in the phase of planning to prevent them from colliding with obstacles as they move. Simulation results show that the proposed method performs better than the grid-based A* algorithm in terms of the search time and space and that the resulting paths are more realistic.

Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Miloudi, Salim,Rahal, Sid Ahmed,Khiat, Salim Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

Vulnerable Path Attack and its Detection

( Chuyu She ),( Wushao Wen ),( Quanqi Ye ),( Kesong Zheng ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.4

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user`s session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Salim Miloudi,Sid Ahmed Rahal,Salim Khiat 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,when a multi-branch company needs to explore its distributed data for decision making, it is imperative toclassify these multiple databases into similar clusters before analyzing the data. To search for the bestclassification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification aresubsets of clusters in the next classification), existing algorithms generate each classification independently,that is, without taking into account the use of clusters from the previous classification. Consequently, existingalgorithms are time consuming, especially when the number of candidate classifications increases. Toovercome the latter problem, we propose in this paper an efficient approach that represents the problem ofclassifying the multiple databases as a problem of identifying the connected components of an undirectedweighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of ouralgorithm against existing works and that it overcomes the problem of increase in the execution time.

An Improved Multi-Rules-Based ACO Algorithm for FJSS Problem in Cloud Manufacturing Environment

Hongguo Zhang,Linyan He,Chao Ma,Shuli Zhang,Shenghui Liu 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.7

In cloud manufacturing environment, for a scheduling Job, there may be a lot of servicizing manufacturing resources and manufacturing capability that can be used to support for its realizing. Therefore, how to efficiently solve FJSS problem becomes more complex and significant. First, this paper uses disjunctive graph model to analyze the characteristic of FJSS problems, and then, focusing on machine selection sub problem, this paper designs multi-rules to solve machine selection conflicts in different scenarios. Finally, on this basis, an improved multi-rules-based ACO algorithm is proposed. The algorithm is applied to the typical examples of the flexible job-shop scheduling problem. Compared with other algorithms, final experimental results indicate that this algorithm is effective.

모델 속성 정보와 그래프 비교 알고리즘을 이용한 그래프 변환 기반 의미 정확성 검증 메커니즘

고종원(Jong-Won Ko),홍봉화(Bong-Hwa Hong),송영재(Young-Jae Song) 한국정보기술학회 2012 한국정보기술학회논문지 Vol.10 No.4

소프트웨어 모델변환에서의 모델 속성 정보를 이용한 그래프 변환 기반 의미 정확성 검증 메카니즘 연구

고종원(Jong-Won Ko),홍봉화(Bong-Haw Hong),송영재(Young-Jae Song) 한국정보기술학회 2011 Proceedings of KIIT Conference Vol.2011 No.5

소프트웨어 개발 효율성을 높이기 위해 모델 차원에서의 재사용성을 목적으로 한 OMG의 MDA 의 모델 변환기술에 대한 최근연구는 입력모델과 목표모델 간의 그래프 비교를 통한 변환모델에 대한 검증방법이나 그래프 패턴을 이용한 모델검증은 두 모델의 구조를 단순히 Version Tree 형태로 모델을 나타내고 있으며, 단순노드와 에지를 통한 상호 그래프 모델을 비교함으로써 실제 설계모델 자체의 구조적인 특징이나 모델이 지니는 다양한 형태의 정보를 정확히 나타내기에 한계점이 많다. 본 논문에서의 모델속성정보를 정의하여 그래프 변환 모델을 다시 재정의하고 기존의 그래프 기반 비교 알고리즘을 개선하여 소프트웨어 모델의 의미정확성을 검증할 수 있는 메카니즘을 제안하고 있다. 또한 AGG 변환도구를 이용한 사례연구를 통해 UML 클래스다이어그램과 RDBMS모델 간의 그래프기반 모델변환에서의 의미정확성을 보장하기 위한 그래프변환규칙 및 그래프 모델속성정의를 적용한다. The existing model transformation tools have focused on various transformation format supports, scalability or applicability of model transformation mechanism itself or how it is easy to understand transformation rules without verification of the transformation model generated through mainly model transformation engines. we supported verification mechanism of transformation model with model property information, dynamic analysis in this paper. Therefore, by increasing reliability of model transformation and furtherapplying test issues on the software development process to the software mode at software design phase. A case study in AGG tool is presented to illustrate the feasibility of the model transformation verification with model property information.