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Hybrid deformable models for medical segmentation and registration

Hybrid deformable models for medical segmentation and regist...

引用

9th international conference on Control, automation, robotics and Vision

作者： Metaxas, Dimitris N. Qian, Zhen Huang, Xiaolei Huang, Rui Chen, Ting Axel, Leon Rutgers State Univ Ctr Computat Biomed Imaging & Modeling Piscataway NJ 08854 USA NYU Dept Radiol New York NY 10016 USA

ISBN: (纸本)9781424403417

Deformable models have bad great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initialization process and make improvements in segmentation and registration. In this paper we present several hybrid deformable methods we have been developing for segmentation and registration. these methods include Metamorphs, a novel shape and texture integration deformable model framework and the integration of deformable models with graphical models and learning methods. We first present a framework for the robust segmentation and tracking of the heart from tagged MRI images and second applications involving brain tumor segmentation as well as brain and cardiac shape registration.

关键词： hybrid deformable model segmentation registration

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A soft sensor based on multiple neural networks combined with two information fusion methods

A soft sensor based on multiple neural networks combined wit...

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9th international conference on Control, automation, robotics and Vision

作者： Ye, Tao Zhu, Xuefeng Li, Xiangyang Li, Yan Zeng, Jun S China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9781424403417

Soft sensors are especially required in lots of advanced process control applications. the ANN based soft sensor are widely studied recently. But the ANN is an uncertain method in nature. In view of the complexity of the industrial processes, the robustness is an important criterion to evaluate a model. the generalization capability is another factor to affect the applicability of a model. Aiming at improving the robustness and generalization capability of a system, a two-level architecture MNN model is proposed for soft sensor modeling. In our model, multiple networks are combined with the Bayesian and Fuzzy C-means (FCM) clustering combination methods at different levels. Two experiments are conducted to validate the effectiveness of our model. the results reveal that the proposed model exceeds other three models indeed.

关键词： soft sensing multiple neural networks fuzzy C-means clustering Bayesian method pulp kappa number

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Terrain modeling using machine learning methods

Terrain modeling using machine learning methods

引用

9th international conference on Control, automation, robotics and Vision

作者： Yeu, Chee-Wee thomas Lim, Meng-Hiot Huang, Guang-Bin Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9781424403417

the problem of terrain modeling is basically a type of function approximation problem. this type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of training a neural network, the resulting high cost in computational time limits the implementation of machine learning based methods in many real world applications (for example, navigation applications in unmanned aerial vehicles) that require fast generation of terrain models. A recently proposed machine learning method, the extreme learning machine (ELM), is able to train single-layer feed forward neural networks with excellent speed and good generalization. In this paper, we present terrain modeling using various machine learning methods, and we compare the performances of these methods with ELM. We also present a comparison of terrain modeling performances between ELM and the popular choice of terrain and surface modeling technique, the Delaunay triangulation with linear interpolation. Our results show that machine learning using ELM offers a potential solution to terrain modeling problems with good performances.

关键词： machine learning back propagation ELM terrain modeling delaunay triangulation

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Automatic 3D segmentation of the liver from computed tomography images, a discrete deformable model approach

Automatic 3D segmentation of the liver from computed tomogra...

引用

9th international conference on Control, automation, robotics and Vision

作者： Evans, A. Lambrou, T. Linney, A. D. Todd-Pokropek, A. UCL Dept Med Phys & Bioengn Malet Pl Engn BldgGower St London WC1E 6BT England

ISBN: (纸本)9781424403417

Automatic segmentation of the liver has the potential to assist in the diagnosis of disease, preparation for organ transplantation, and possibly assist in treatment planning. this paper presents initial results from work that extends on previous two-dimensional (2D) segmentation methods by implementing full three-dimensional (3D) liver segmentation, using a self-reparameterising discrete deformable model. this method overcomes many of the weaknesses inherent in 2D segmentation techniques, such as the inability to automatically segment separate lobes of the liver in each image slice, and sensitivity to individual-slice noise. Results are presented showing volumetric and overlap comparison of twelve automatically segmented livers with their corresponding manually segmented livers, which were treated as the gold standard for this study.

关键词： 3D segmentation 3D deformable models liver adaptive remeshing

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On the implementation of a speech recognizer for Romanian language based on Hidden Markov models

On the implementation of a speech recognizer for Romanian la...

引用

IEEE-TTTC international conference on automation, Quality and Testing, robotics

作者： Caruntu, Alexandru Tech Univ Cluj Napoca Dept Telecommun 26-28 Baritiu Str Cluj Napoca Romania

ISBN: (纸本)142440360X

Over the last decades there has been made a considerable progress in the field of automatic speech recognition. New methods and technologies have been developed and we can say that we have currently reached a state-of-the-art in this field. Most of the latest speech recognizers are based on Hidden Markov models since it has been proved that this is the best method to model speech. this paper presents the steps involved in building this kind of a speech recognition system, together with a few results obtained in the recognition of isolated words (digits from zero to nine) in Romanian language.

关键词： Hidden Markov models

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Hybrid Deformable models for Medical Segmentation and Registration

Hybrid Deformable Models for Medical Segmentation and Regist...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Dimitris N Metaxas Zhen Qian Xiaolei Huang Rui Huang Ting Chen Leon Axel Center for Computational Biomedicine Imaging and Modeling Rutgers University Piscataway NJ USA Department of Radiology New York University NY USA

Deformable models have had great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initialization process and make improvements in segmentation and registration. In this paper we present several hybrid deformable methods we have been developing for segmentation and registration. these methods include metamorphs, a novel shape and texture integration deformable model framework and the integration of deformable models with graphical models and learning methods. We first present a framework for the robust segmentation and tracking of the heart from tagged MRI images and second applications involving brain tumor segmentation as well as brain and cardiac shape registration

关键词： Deformable models Image segmentation Shape Biomedical imaging Medical services Biomedical equipment Graphical models Learning systems Robustness Heart

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Validation of FE Deformation models using Ultrasonic and MR Images

Validation of FE Deformation Models using Ultrasonic and MR ...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Junji Muramatsu Takashi Ikuta Shinichi Hirai Shigehiro Morikawa Department Robotics Ritsumeikan University Kusatsu Shiga Japan Shiga University of Medical Science Otsu Shiga Japan

this paper describes the measurement of inner deformation of a rheological object using ultrasonic and MR images and comparison the measured and simulated deformations. We apply finite element (FE) model to simulate elastic, viscoplastic, and rheological deformation of soft objects. Ultrasonic and MR images are used to reveal the inner deformation of a soft object. Here we report the measurement and its evaluation by comparing measured and simulated deformations

关键词： Iron Deformable models Biological system modeling Ultrasonic variables measurement Rheology Computational modeling Shape Finite element methods Surgery Humans

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Model and Fault Inference with the Framework of Probabilistic SDG

Model and Fault Inference with the Framework of Probabilisti...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Fan Yang Deyun Xiao Department of Automation Tsinghua University Beijing China

As the scale of systems increases, traditional models and fault diagnosis methods are not applicable. Qualitative signed directed graphs (QSDG) are used to model the variables and relationships among them in large-scale complex systems. However, they have distinct limitations of resulting spurious solutions due to the lack of utilization of knowledge or information. this article proposes a kind of probabilistic SDG (PSDG) model to describe the propagation of faults among variables. the fault diagnosis method is also investigated, where Bayesian network has been employed. Finally, examples are given and the future topics are listed

关键词： Fault diagnosis Large-scale systems Inference algorithms Hazards automation Bayesian methods Tree graphs Sufficient conditions Industrial relations Equations

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Adaptive Control for Stabilizing Nonnecessarily Inversely Stable Plants by Using Multiple Estimation models with Multirate Input and Fractional-Order Holds

Adaptive Control for Stabilizing Nonnecessarily Inversely St...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： S. Alonso-Quesada M. De la Sen Departamentode Electricidad y Electronica Universidad del Paris Vasco Spain Departamento de Electricidad y Electrónica Universidad del País Vasco Leioa Spain

this paper presents a multi-estimation adaptive control strategy for stabilizing a potentially noninversely stable, linear and time-invariant plant. Such a strategy works with several discretization models of the plant. Each one of them is obtained by means of a fractional-order hold (FROH) and a multirate input in order to place its zeros into the stability region. A supervisor activates one of such models and maintains it in operation during at least a minimum residence time for stability purposes. that estimation model parameterizes a discrete-time adaptive controller for asymptotically matching a stable reference model at sampling instants

关键词： Adaptive control Stability Sampling methods Programmable control Parameter estimation Signal processing Switches Transient response State-space methods Equations

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A Soft Sensor Based on Multiple Neural Networks Combined with Two Information Fusion methods

A Soft Sensor Based on Multiple Neural Networks Combined wit...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Tao Ye Xuefeng Zhu Xiangyang Li Yan Li Jun Zeng College of Automation Science and Engineering South China University of Technology Guangzhou China

Soft sensors are especially required in lots of advanced process control applications. the ANN based soft sensor are widely studied recently. But the ANN is an uncertain method in nature. In view of the complexity of the industrial processes, the robustness is an important criterion to evaluate a model. the generalization capability is another factor to affect the applicability of a model. Aiming at improving the robustness and generalization capability of a system, a two-level architecture MNN model is proposed for soft sensor modeling. In our model, multiple networks are combined with the Bayesian and fuzzy C-means (FCM) clustering combination methods at different levels. Two experiments are conducted to validate the effectiveness of our model. the results reveal that the proposed model exceeds other three models indeed

关键词： Sensor fusion Neural networks Multi-layer neural network Artificial neural networks Robustness Process control Bayesian methods Electrical equipment industry Artificial intelligence Predictive models

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