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A shape-driven MRF model for the segmentation of organs in medical images

A shape-driven MRF model for the segmentation of organs in m...

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conference on computer vision and pattern recognition (CVPR)

作者： D. R. Chittajallu S. K. Shah I. A. Kakadiaris Computational Biomedicine Lab Depts. of Computer Science Elec. & Comp.Engineering and Biomedical Engineering University of Houston Houston TX USA

In this paper, we present a knowledge-driven Markov Random Field (MRF) model for the segmentation of organs in medical images with particular emphasis on the incorporation of shape constraints into the segmentation problem. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. We then incorporate a set of constraints into the Gibbs energy function that collectively force the resulting segmentation contour/surface to have a shape similar to that of a given shape template. In particular, we introduce a flux-maximization constraint and a generalized template-based star-shape constraint that are encoded into the first- and second-order clique potentials of the Gibbs energy function, respectively. Our main contribution is in the translation of a set of global notions about the shape of the desired segmentation contour into a set of local measures that can be conveniently encoded into the Gibbs energy function and used in combination with other traditionally used constraints derived from image information. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography (CT) data.

关键词： Image segmentation Biomedical imaging Markov random fields Shape measurement Computed tomography Biomedical engineering Energy measurement Heart Biomedical computing computer science

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Image atlas construction via intrinsic averaging on the manifold of images

Image atlas construction via intrinsic averaging on the mani...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuchen Xie Jeffrey Ho Baba C. Vemuri Department of Computer and Information Sciences and Engineering University of Florida USA

In this paper, we propose a novel algorithm for computing an atlas from a collection of images. In the literature, atlases have almost always been computed as some types of means such as the straightforward Euclidean means or the more general Karcher means on Riemannian manifolds. In the context of images, the paper's main contribution is a geometric framework for computing image atlases through a two-step process: the localization of mean and the realization of it as an image. In the localization step, a few nearest neighbors of the mean among the input images are determined, and the realization step then proceeds to reconstruct the atlas image using these neighbors. Decoupling the localization step from the realization step provides the flexibility that allows us to formulate a general algorithm for computing image atlas. More specifically, we assume the input images belong to some smooth manifold M modulo image rotations. We use a graph structure to represent the manifold, and for the localization step, we formulate a convex optimization problem in R k (k the number of input images) to determine the crucial neighbors that are used in the realization step to form the atlas image. The algorithm is both unbiased and rotation-invariant. We have evaluated the algorithm using synthetic and real images. In particular, experimental results demonstrate that the atlases computed using the proposed algorithm preserve important image features and generally enjoy better image quality in comparison with atlases computed using existing methods.

关键词： computer vision Manifolds Nearest neighbor searches Image reconstruction Image quality Biomedical imaging Equations Symmetric matrices Geometry

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Multiple object detection by sequential monte carlo and Hierarchical Detection Network

Multiple object detection by sequential monte carlo and Hier...

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conference on computer vision and pattern recognition (CVPR)

作者： Michal Sofka Jingdan Zhang S. Kevin Zhou Dorin Comaniciu Siemens AG Corporate Research and Development Princeton NJ USA

In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images. Since a joint multi-object model is difficult to obtain in most practical situations, we focus here on detecting the objects sequentially, one-by-one. The interdependence of object poses and strong prior information embedded in our domain of medical images results in better performance than detecting the objects individually. Our approach is based on Sequential Estimation techniques, frequently applied to visual tracking. Unlike in tracking, where the sequential order is naturally determined by the time sequence, the order of detection of multiple objects must be selected, leading to a Hierarchical Detection Network (HDN). We present an algorithm that optimally selects the order based on probability of states (object poses) within the ground truth region. The posterior distribution of the object pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple objects and hierarchical levels. We show on 2D ultrasound images of left atrium, that the automatically selected sequential order yields low mean detection error. We also quantitatively evaluate the hierarchical detection of fetal faces and three fetal brain structures in 3D ultrasound images.

关键词： Object detection Monte Carlo methods Performance evaluation History Educational institutions Machine vision Radio frequency System testing

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Segmenting video into classes of algorithm-suitability

Segmenting video into classes of algorithm-suitability

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conference on computer vision and pattern recognition (CVPR)

作者： Oisin Mac Aodha Gabriel J. Brostow Marc Pollefeys University College London UK ETH Zurich Switzerland

Given a set of algorithms, which one(s) should you apply to, i) compute optical flow, or ii) perform feature matching? Would looking at the sequence in question help you decide? It is unclear if even a person with intimate knowledge of all the different algorithms and access to the sequence itself could predict which one to apply. Our hypothesis is that the most suitable algorithm can be chosen for each video automatically, through supervised training of a classifier. The classifier treats the different algorithms as black-box alternative “classes,” and predicts when each is best because of their respective performances on training examples where ground truth flow was available. Our experiments show that a simple Random Forest classifier is predictive of algorithm-suitability. The automatic feature selection makes use of both our spatial and temporal video features. We find that algorithm-suitability can be determined per-pixel, capitalizing on the heterogeneity of appearance and motion within a video. We demonstrate our learned region segmentation approach quantitatively using four available flow algorithms, on both known and novel image sequences with ground truth flow. We achieve performance that often even surpasses that of the one best algorithm at our disposal.

关键词： Image motion analysis Optical computing Image segmentation Testing Prediction algorithms Image sequences Stereo vision Gold Measurement standards Educational institutions

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Vote-based LELC for positive and unlabeled textual data streams

Vote-based LELC for positive and unlabeled textual data stre...

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10th ieee International conference on Data Mining workshops, ICDMW 2010

作者： Liu, Bo Xiao, Yanshan Cao, Longbing Yu, Philip S. Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and IT University of Technology Sydney Australia Department of Computer Science University of Illinois at Chicago 851 S. Morgan Street Chicago IL 60607-0753 United States

ISBN: (纸本)9780769542577

In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works in three steps. In the first step, we extract representative documents from unlabeled data and assign a vote score to each document. The assigned vote score reflects the degree of belongingness of an example towards its corresponding class. In the second step, the extracted representative examples, together with their vote scores, are incorporated into a learning phase to build an SVM-based classifier. In the third step, we propose the usage of an ensemble classifier to cope with concept drift involved in the textual data stream environment. Our developed approach aims at improving the performance of LELC by rendering examples to contribute differently to the construction of the classifier according to their vote scores. Extensive experiments on textual data streams have demonstrated that vote-based LELC outperforms the original LELC method. © 2010 ieee.

关键词： Data Streams LELC Positive and Unlabeled Learning SVM-based classifier data structures document extraction documents representation learning learning by extracting likely positive and negative micro-cluster pattern classification support vector machines t

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Visual tracking via weakly supervised learning from multiple imperfect oracles

Visual tracking via weakly supervised learning from multiple...

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conference on computer vision and pattern recognition (CVPR)

作者： Bineng Zhong Hongxun Yao Sheng Chen Rongrong Ji Xiaotong Yuan Shaohui Liu Wen Gao Harbin Institute of Technology Heilongjiang China Department of Electrical and Computer Engineering National University of Singapore Singapore Peking University Beijing China

Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most top-performing methods rely on online learning-based algorithms. Unfortunately, one inherent problem of online learning-based trackers is drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., trackers), some of which may be mediocre. A probabilistic approach is proposed to simultaneously infer the most likely object position and the accuracy of each tracker. Moreover, an online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and fast. Consequently, the proposed method can avoid the pitfalls of purely single tracking approaches and get reliable labeled samples to incrementally update each tracker (if it is an appearance-adaptive tracker) to capture the appearance changes. Extensive comparing experiments on challenging video sequences demonstrate the robustness and effectiveness of the proposed method.

关键词： Supervised learning Target tracking Robustness Intelligent robots Layout Training data Video sequences Machine intelligence Video surveillance Humans

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Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures

Multiple dynamic models for tracking the left ventricle of t...

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conference on computer vision and pattern recognition (CVPR)

作者： Gustavo Carneiro Jacinto C. Nascimento Instituto de Sistemas e Robótica Instituto Superior Técnico Lisboa Portugal

The problem of automatic tracking and segmentation of the left ventricle (LV) of the heart from ultrasound images can be formulated with an algorithm that computes the expected segmentation value in the current time step given all previous and current observations using a filtering distribution. This filtering distribution depends on the observation and transition models, and since it is hard to compute the expected value using the whole parameter space of segmentations, one has to resort to Monte Carlo sampling techniques to compute the expected segmentation parameters. Generally, it is straightforward to compute probability values using the filtering distribution, but it is hard to sample from it, which indicates the need to use a proposal distribution to provide an easier sampling method. In order to be useful, this proposal distribution must be carefully designed to represent a reasonable approximation for the filtering distribution. In this paper, we introduce a new LV tracking and segmentation algorithm based on the method described above, where our contributions are focused on a new transition and observation models, and a new proposal distribution. Our tracking and segmentation algorithm achieves better overall results on a previously tested dataset used as a benchmark by the current state-of-the-art tracking algorithms of the left ventricle of the heart from ultrasound images.

关键词： Particle tracking Heart Ultrasonic imaging Particle filters Distributed computing Image segmentation Proposals computer architecture Filtering algorithms Monte Carlo methods

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The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors

The automatic design of feature spaces for local image descr...

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conference on computer vision and pattern recognition (CVPR)

作者： Gustavo Carneiro Instituto de Sistemas e Robótica Instituto Superior Técnico Lisboa Portugal

The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classification and image matching. In order to be useful, these descriptors have to present a good trade off between discriminating power and robustness to typical image deformations. The feature spaces of the most useful local descriptors have been manually designed based on the goal above, but this design often limits the use of these descriptors for some specific matching and visual classification problems. Alternatively, there has been a growing interest in producing feature spaces by an automatic combination of manually designed feature spaces, or by an automatic selection of feature spaces and spatial pooling methods, or by the use of distance metric learning methods. While most of these approaches are usually applied to specific matching or classification problems, where test classes are the same as training classes, a few works aim at the general feature transform problem where the training classes are different from the test classes. The hope in the latter works is the automatic design of a universal feature space for local descriptor matching, which is the topic of our work. In this paper, we propose a new incremental method for learning automatically feature spaces for local descriptors. The method is based on an ensemble of non-linear feature extractors trained in relatively small and random classification problems with supervised distance metric learning techniques. Results on two widely used public databases show that our technique produces competitive results in the field.

关键词： Feature extraction Robustness Extraterrestrial measurements Image matching Shape Testing computer vision Learning systems Learning automata Image databases

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Measuring visual saliency by Site Entropy Rate

Measuring visual saliency by Site Entropy Rate

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conference on computer vision and pattern recognition (CVPR)

作者： Wei Wang Yizhou Wang Qingming Huang Wen Gao Chinese Academy and Sciences Beijing China Key Laboratory of Machine Perception MoE Peking University Beijing China

In this paper, we propose a new computational model for visual saliency derived from the information maximization principle. The model is inspired by a few well acknowledged biological facts. To compute the saliency spots of an image, the model first extracts a number of sub-band feature maps using learned sparse codes. It adopts a fully-connected graph representation for each feature map, and runs random walks on the graphs to simulate the signal/information transmission among the interconnected neurons. We propose a new visual saliency measure called Site Entropy Rate (SER) to compute the average information transmitted from a node (neuron) to all the others during the random walk on the graphs/network. This saliency definition also explains the center-surround mechanism from computation aspect. We further extend our model to spatial-temporal domain so as to detect salient spots in videos. To evaluate the proposed model, we do extensive experiments on psychological stimuli, two well known image data sets, as well as a public video dataset. The experiments demonstrate encouraging results that the proposed model achieves the state-of-the-art performance of saliency detection in both still images and videos.

关键词： Entropy Biological system modeling Videos Biology computing Computational modeling Neurons Biological information theory Data mining computer networks Psychology

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Piecewise planar and non-planar stereo for urban scene reconstruction

Piecewise planar and non-planar stereo for urban scene recon...

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conference on computer vision and pattern recognition (CVPR)

作者： David Gallup Jan-Michael Frahm Marc Pollefeys Department of Computer Science North Carolina State University Chapel Hill NC USA Department of Computer Science ETH Zürich Zurich Switzerland

Piecewise planar models for stereo have recently become popular for modeling indoor and urban outdoor scenes. The strong planarity assumption overcomes the challenges presented by poorly textured surfaces, and results in low complexity 3D models for rendering, storage, and transmission. However, such a model performs poorly in the presence of non-planar objects, for example, bushes, trees, and other clutter present in many scenes. We present a stereo method capable of handling more general scenes containing both planar and non-planar regions. Our proposed technique segments an image into piecewise planar regions as well as regions labeled as non-planar. The non-planar regions are modeled by the results of a standard multi-view stereo algorithm. The segmentation is driven by multi-view photoconsistency as well as the result of a color-and texture-based classifier, learned from hand-labeled planar and non-planar image regions. Additionally our method links and fuses plane hypotheses across multiple overlapping views, ensuring a consistent 3D reconstruction over an arbitrary number of images. Using our system, we have reconstructed thousands of frames of street-level video. Results show our method successfully recovers piecewise planar surfaces alongside general 3D surfaces in challenging scenes containing large buildings as well as residential houses.

关键词： Layout Image reconstruction Semiconductor device modeling Image segmentation Stereo vision Surface reconstruction Surface texture Surface fitting Stereo image processing Rendering (computer graphics)

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