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Otsu multi-thresholding based on DCG3A for medical image

ICIC Express Letters, Part B: Applications 2016年第8期7卷 1651-1656页

作者： Fang, Zheng Shen, Xuanjing Chen, Haipeng Feng, Yuncong College of Computer Science and Technology Jilin University No. 2699 Qianjin Ave. Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Ave. Changchun130012 China

In order to solve the problem that multi-thresholding segmentation spends too much time finding the optimal solution in medical image segmentation, Otsu multi-thresholding based on dynamic combination of genetic algorithm and ant algorithm (abbreviated as DCG3A-OM) was proposed in this paper. Firstly, the algorithm uses genetic algorithm to generate preliminary partition results, and then converts them into the initial information of ant colony. Finally ant algorithm will approach to optimal values efficiently. While running genetic algorithm, the conditions of combination will be analyzed dynamically to avoid stopping genetic algorithm early or late. Besides, it increases the probability of variation and decreases the numbers of individuals which are calculated for the evolution rate of colony. The experimental results demonstrate that DCG3A-OM gets the optimal solution more quickly and efficiently than the others, and it can always get the optimal values mostly. © 2016 ICIC International.

关键词： Genetic algorithms

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Heterogeneous data mining for planning active surveillance of malaria 15

Heterogeneous data mining for planning active surveillance o...

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ASE BigData and SocialInformatics, ASE BD and SI 2015

作者： Gu, Xiao Chen, Hechang Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781450337359

Malaria is one of the most serious diseases in the world, which is densely distributed in poverty and remote areas. In the prevention and control of malaria, active surveillance is more efficient than passive surveillance to discover the incidences timely and accurately. However, it is always faced with the challenge of how to allocate the limited sources, such as medical staff and medicine, appropriately so as to achieve a maximum infect. In this paper, we propose a novel method to characterize the spatiotemporal patterns of infection risk for active surveillance planning. Specifically, we propose a temporal heterogeneous diffusion network model to discover high risk areas timely, and a mixture optimization method to find high risk areas accurately. The validation on existing real-world data shows that our method outperforms the existing state-of-the-art both in terms of infection risk prediction and planning of active surveillance under different thresholds. © 2015 ACM.

关键词： Mixtures

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A novel method to measure graph similarity 12

A novel method to measure graph similarity

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12th IEEE International Conference on E-Business engineering, ICEBE 2015

作者： Wang, Xu Ouyang, Jihong College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781467380027

A novel method to measure the graph similarity is proposed, where the labels, in-degrees, and out-degrees of the vertices in the graph are comprehensively considered in order to conquer the high complexity and information loss in the measurement process. In this proposal, the graph is decomposed into a generalized tree and its similarity can be measured by computing the cost of the node sequence triples in the generalized tree. The proposed method decreases the computational complexity to O(n2) in comparison with the other available graph similarity methods. An example is provided to illustrate that the method is feasible and effective. © 2015 IEEE.

关键词： Forestry

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A bio-inspired method for locating the diffusion source with limited observers

A bio-inspired method for locating the diffusion source with...

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Congress on Evolutionary Computation

作者： Yuxin Liu Chao Gao Xinyan She Zili Zhang College of Computer and Information Science & College of Software Southwest University Chongqing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Information Technology Deakin University Geelong VIC Australia

ISBN: (纸本)9781509006243

Locating the source of diffusion is a challenging problem in complex networks and has great practical significance for restraining rumors propagation and controlling epidemics spreading. An efficient locating method should have a higher locating accuracy with the minimum required information. Although existing locating methods based on observers consider the time delays of edges, they compute the time delays based on the shortest path, which may differ from the actual diffusion process. Moreover, the higher locating accuracy of traditional method with observers has a great dependence on the assumption that the propagation delays along edges follow a definite distribution such as the Gaussian distribution. In order to solve these shortcomings, this paper proposes a Physarum-inspired method to locate the diffusion source that is independence of the distribution of propagation delays. Our method quantifies the nutrient transportation process in the adaptive network evolved by Physarum, which is used to simulate the information or epidemic diffusion routes in a social network. Simulation results on various benchmark networks show that our method has a better performance in terms of error distance than that of Gaussian method without assuming the definite distribution of time delays. Together with the advantage that our method does not require the sender information of observers compared with existing methods, our method allows for a wider range of applications in the real-world networks.

关键词： Electron tubes Observers Diffusion processes Adaptive systems Propagation delay Conductivity

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 IEEE International Conference on Progress in Informatics and computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT key-points for Iris Recognition

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Journal of Bionic engineering 2015年第3期12卷 504-517页

作者： Yuanning Liu Fei He Xiaodong Zhu Zhen Liu Ying Chen Ye Han Lijiao Yu College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Chanschun 130012 China Graduate School of Engineering Nagasaki Institute of Applied Science Japan College of Software Nanchang Hangkong University Nanchang 330063 China

Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation （SIFT）, which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods.

关键词： iris recognition bionic Gabor features scale invariant feature transformation support vector regression score level fusion

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Entailment-based axiom pinpointing in debugging incoherent terminologies 1

Entailment-based axiom pinpointing in debugging incoherent t...

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1st International Workshop on Semantic Technologies, IWOST 2015

作者： Ye, Yuxin Ouyang, Dantong Su, Jing College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

One of the major problems of axiom pinpointing for incoherent terminologies is the precise positioning within the conflict axioms. In this paper we present a formal notion for the entailment-based axiom pinpointing of incoherent terminologies, where the parts of an axiom is defined by atomic entailment. Based on these concepts, we prove the one-to-many relationship between existing axiom pinpointing with the entailment-based axiom pinpointing. For its core task, calculating minimal unsatisfiable entailment, we provide algorithms for OWL DL terminologies using incremental strategy and Hitting Set Tree algorithm. The feasibility of our method is shown by case study and experiment evaluations.

关键词： Terminology

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Designment of E-R model based on RDF (S) 2

Designment of E-R model based on RDF (S)

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2nd International Workshop on Semantic Technologies, IWOST 2015

作者： Gao, Xiangbin Ouyang, Dantong Ye, Yuxin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Based on the vast domain resources of RDF (S) on the web and SPARQL's powerful query ability, this article presents a new method of designment of E-R model. The steps for this design are: (1) Formu- lating SPARQL rules (including resource query rules and schema query rules) by the analysis of RDF (S)'s structure. (2) Parsing the optimal resource obtained through the query sentences. (3) Completing the de- signment by taking advantages of the translation from RDF (S) model to entity-relationship model in accordance with the content queried. The re- sults indicate that, the designment of E-R model based on RDF (S) could restore user real requirements of great possibilities and help database de- signer to complete design in a strange area.

关键词： Syntactics

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Location based surveillance for early detection of contagious outbreaks

Location based surveillance for early detection of contagiou...

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ACM International Joint Conference on Pervasive and Ubiquitous computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015

作者： Bai, Yuan Yang, Bo Du, Zhanwei Meyers, Lauren Ancel School of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China University of Texas at Austin AustinTX73301 United States

ISBN: (纸本)9781450335751

In previous studies, non-distance-dependent surveillance strategies have improved the performance of contagious outbreaks detection. In this paper, we propose a new distance-dependent strategy that does not require ascertainment of global or local network structure, namely, simply monitoring the relative significance difference of randomly selected individuals in school and workplace. To evaluate whether such two group could indeed provide early detection, we studied a flu outbreak in contact network simulation experiments. Our experimental results show that this method could provide significant additional time to react to epidemics, especially when the infection rate is not large.

关键词： Wearable technology

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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