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3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2012

作者： Langs, Georg Hanbury, Allan Menze, Bjoern Müller, Henning Computational Image Analysis and Radiology Lab. Department of Radiology Medical University of Vienna Austria Institute for Software Technology and Interactive Systems Faculty of Computer Science Vienna University of Technology Austria Computer Vision Lab. ETH Zurich Switzerland Switzerland

ISBN: (纸本)9783642366772

The increasing amount of medical imaging data acquired in clinical practice holds a tremendous body of diagnostically relevant information. Only a small portion of these data are accessible during clinical routine or research due to the complexity, richness, high dimensionality and size of the data. There is consensus in the community that leaps in this regard are hampered by the lack of large bodies of data shared across research groups and an associated definition of joint challenges on which development can focus. In this paper we describe the objectives of the project Visceral. It will provide the means to jump-start this process by providing access to unprecedented amounts of real world imaging data annotated through experts and by using a community effort to generate a large corpus of automatically generated standard annotations. To this end, Visceral will conduct two competitions that tackle large scale medical image data analysis in the fields of anatomy detection, and content-based image retrieval, in this case the retrieval of similar medical cases using visual data and textual radiology reports. © 2013 Springer-Verlag.

关键词： Medical imaging

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image retrieval based on bit-plane distribution feature

Image retrieval based on bit-plane distribution feature

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6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

作者： Shan, Zhao Jing, Lui School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Prov. Key Lab. on Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China

ISBN: (纸本)9780769537351

Based on the analysis of color histogram for image retrieval, a new descriptor, bit-plane distribution feature(BPDF), is proposed in this paper. The image is firstly divided into eight bit-planes. Meantime, the Gray code of bit-planes is used to avoid the effect of changes in the intensity values on bit-planes. Then, according to the distribution of each bit-plane, a feature vector is constructed by the first four significant planes which contain most of the structural information of the image. Finally, the Mahalanobis distance is adopted to measure the similarity because of the correlation between the concerned vectors after designing a correlation-weighted matrix. Comparisons are conducted between BPDF and other descriptors. Experimental results show that the proposed method provides more significantly retrieval results than the traditional ones. © 2009 IEEE.

关键词： image retrieval

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Adaptive resampling in particle filter based on diversity measures

Adaptive resampling in particle filter based on diversity me...

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International Conference on Computer science and Education

作者： Yu, Jin-Xia Liu, Wen-Jing Tang, Yong-Li College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Provincial Key Lab. of Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781424460052

It is very important to find a criterion when to perform a resampling step. Aimed at this problem, an adaptive resampling algorithm in particle filter based on diversity measures is presented. Based on the analysis and comparison of two diversity measures that are effective sample size (ESS) and population diversity factor(PDF), the resampling algorithm based on ESS is introduced. On the basis of that, PDF is used for adaptively tuning the resampling threshold. At last, with the simulation program using matlab.7.0 to track a single target motion from a fixed visual observation points, the performance of the proposed algorithm is evaluated and its validity is verified. ©2010 IEEE.

关键词： MATlab

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Robust dense endoscopic stereo reconstruction for minimally invasive surgery

Robust dense endoscopic stereo reconstruction for minimally ...

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15th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2012

作者： Bernhardt, Sylvain Abi-Nahed, Julien Abugharbieh, Rafeef Biomedical Signal and Image Computing Lab. University of British Columbia Vancouver BC Canada Qatar Robotic Surgery Centre Qatar Science and Technology Park Qatar

ISBN: (纸本)9783642366192

Robotic assistance in minimally invasive surgical interventions has gained substantial popularity over the past decade. Surgeons perform such operations by remotely manipulating laparoscopic tools whose motion is executed by the surgical robot. One of the main tools deployed is an endoscopic binocular camera that provides stereoscopic vision of the operated scene. Such surgeries have notably garnered wide interest in renal surgeries such as partial nephrectomy, which is the focus of our work. This operation consists of the localization and removal of tumorous tissue in the kidney. During this procedure, the surgeon would greatly benefit from an augmented reality view that would display additional information from the different imaging modalities availab.e, such as pre-operational CT and intra-operational ultrasound. In order to fuse and visualize these complementary data inputs in a pertinent way, they need to be accurately registered to a 3D reconstruction of the imaged surgical scene topology captured by the binocular camera. In this paper we propose a simple yet powerful approach for dense matching between the two stereoscopic camera views and for reconstruction of the 3D scene. Our method adaptively and accurately finds the optimal correspondence between each pair of images according to three strict confidence criteria that efficiently discard the majority of outliers. Using experiments on clinical in-vivo stereo data, including comparisons to two state-of-the-art 3D reconstruction techniques in minimally invasive surgery, our results illustrate superior robustness and better suitability of our approach to realistic surgical applications. © 2013 Springer-Verlag.

关键词： Augmented reality

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Fast non-rigid image registration using viscous fluid model and B-spline

Fast non-rigid image registration using viscous fluid model ...

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2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Rong, Chengcheng Zhou, Jian Luo, Limin Cao, Guogang Lab. of Image Science and Technology Southeast University 210096 Nanjing China INSERM U642 LTSI Université de Rennes 1 35042 France

ISBN: (纸本)9781424417483

Viscous fluid based non-rigid registration algorithm is an appropriate method for registering objects with large-scale difference. The core component of the algorithm is to solve the fluid partial differential equations. Usually, iterative methods, such as successive over-relaxation, are employed to obtain the solution, but the computational complexity is very tremendous. To reduce the complexity, a fast method based on fluid model combined with B-spline is presented. Firstly, B-spline is used to model the velocity fields, so that the large number of unknowns are reduced to fewer B-spline coefficients;Secondly, by use of some special attributes of B-spline and fast Fourier transform(FFT), direct inversion for the B-spline coefficients are deduced. Experimental results show that our new method is not only suitable for the large-scale image deformation but also amounts relatively lower computational costs. © 2008 IEEE.

关键词： image registration

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Improved Local Texture Features for Pedestrian Detection 3

Improved Local Texture Features for Pedestrian Detection

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3rd IEEE International Conference on image, Vision and Computing, ICIVC 2018

作者： Ling, Xiao Yongjun, Zhang Qian, Wang Yuewei, Li College of Computer Science and Technology Key Lab. of Intelligent Med. Image Anal. and Precise Diagn. of Guizhou Province Guizhou University Guiyang China

ISBN: (纸本)9781538649916

Pedestrian detection is a hot issue in the field of computer vision and image processing in recent years. It has important application value in the domain of unmanned cars and driver assistance systems and so on, but there are existed many problems that need to be solved. In this paper, we present an improved texture feature MLBP (Mean of Local Binary Pattern) and the CMLBP (Color based on Mean of Local Binary Pattern) feature based on various color spaces. When the uniform LBP feature does not consider the influence of noise, the mutation of central pixel and neighborhood pixel is not taken into account and therefore the extraction processes of MLBP feature improve the calculation method of the uniform LBP, which makes the extracted feature more stable. The MLBP feature is extracted from gray images, yet color images transformed into gray images generally loss a great amount of information. In view of this point, we also propose the CMLBP feature based on multiple color spaces that is a more comprehensive description of the texture feature of images. In the INRIA pedestrian dataset, many experiments have been conducted with SVM and HIKSVM classifier, and the results manifest that the detection rates of MLBP and CMLBP are much better than the uniform LBP and the basic LBP. The combination of MLBP, CMLBP and other features has been applied to pedestrian detection, which also achieves good results. © 2018 IEEE.

关键词： Local binary pattern

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A point-pattern matching method based on maximal subset of one-to-one mapping

A point-pattern matching method based on maximal subset of o...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Hu, Jing Zhang, Tian-Xu Institute for Pattern Recognition and AI Hua Zhong University of Science and Technology Wuhan 430074 China Key Lab. of Education Ministry for Image Processing and Intelligent Control Hua Zhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

This paper presents a new affine registration approach for planar point pattern matching. A process of parameter space clustering is implemented to confirm a one-to-one mapping between the maximal subsets of feature point sets in images. For a best performance, a coarse parameter space and a fine parameter space are used for vectors comparison. Experiments show that the method can produce positive results from a small number of feature points and intensive noise. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Affine transforms

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Facial feature extraction and face verification

Facial feature extraction and face verification

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Wu, Haiyuan Chen, Qian Yachida, M. Department of Systems Engineering Osaka University 1-3 M achikaneyama-cho Toyonaka Osaka 560 Japan Nara Institute of Science and Technology Japan Lab. Image Information Science and Technolog Japan

ISBN: (纸本)081867282X

The outputs of many face detection systems are face candidates that may contain some false faces. This paper describes an algorithm to verify the face candidates. Once a face candidate is detected from an image, the positions and the sizes of the facial features on the face are predicted based on the knowledge about the arrangement of the facial features on a human face. Then the facial features are detected from the predicted positions with a coarse to fine approach. The face candidates are verified by considering whether facial features can be extracted and how well they match with a relational face model that describes the geometric relationship among the facial features of a generic human face. © 1996 IEEE.

关键词： Feature extraction

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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A probability-based object tracking method

A probability-based object tracking method

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4th International Conference on Intelligence science and Big Data Engineering, IScIDE 2013

作者： Song, Xu Li, Guoqiang Li, Ying Zhang, Yanning Shaanxi Province Key Lab. of Speech and Image Information Processing Northwestern Polytechnical University Xi'an 710129 China Science and Technology on Electro-optic Control Laboratory Louyang 471009 China

ISBN: (纸本)9783642420566

Here we take advantage of the signal recovery power of Compressive Sensing (CS) to significantly reduce the computational complexity brought by the high-dimension image data, then an effective and efficient low-dimensional subspace representation of the object is computing by applying Principal Component Analysis (PCA) to a collection of object observations which are low-dimensional vectors derived from CS. An incremental PCA algorithm is used to update this subspace model for characterizing the object appearance changes. Meanwhile, two distances derived from Probabilistic Principal Component Analysis (PPCA): distance from feature space (DFFS) and distance in feature space (DIFS), are used to describe visual similarity between the learned subspace representation model and candidate targets. Comparing with the traditional used reconstruction error, the sum of two distances: DFFS + DIFS, is more accurate and more robust to noises and partial occlusions. Numerous experiment demonstrate that subspace representation model can handle the situation that target objects experience pose changes, scale changes, significant illumination variation, partial occlusions and so on. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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