A multiscale random field model for Bayesian image segmentation

Bouman, Charles A.; Shapiro, M. V.

doi:10.1109/83.277898

Cited by 525 publications

(431 citation statements)

References 45 publications

(36 reference statements)

Supporting

Mentioning

408

Contrasting

Unclassified

Order By: Relevance

“…To address the problems associated with nonhierarchical models, multiscale MRF models were formulated and have been extensively discussed in the image processing literature (Bouman and Shapiro, 1994;Kato et al 1996Kato et al , 1999Laferté et al, 2000;Liang and Tjahjadi, 2006;Mignotte et al, 2000;Wilson and Li, 2003). In those hierarchical MRF models, there is a series of random fields at a range of scales or resolutions, and the random field at each scale depends only on the next coarser random field above it.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Zhang¹

2007

Bull. Math. Biol.

View full text Add to dashboard Cite

Cell membranes display a range of receptors that bind ligands and activate signaling pathways. Signaling is characterized by dramatic changes in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 nm resolution needed to understand how signaling complexes assemble and function. However, due to experimental limitations, only two molecular species can usually be labeled at a time. A formidable challenge is to integrate experimental data across multiple experiments where there are from 10 to 100 different proteins and lipids of interest and only the positions of two species can be observed simultaneously. As a solution, we propose the use of Markov random field (MRF) modeling to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with neighboring sites in spatial lattices. The presence or absence of a protein of a specific type at a point on the cell membrane is a state. Since only two protein types can be observed, i.e., those bound to particles, and the rest cannot be observed, the problem is one of deducing the conditional distribution of a MRF with unobservable (hidden) states. Here, we develop a multiscale MRF model and use mathematical programming techniques to infer the conditional distribution of a MRF for proteins of three types from observations showing the spatial relationships between only two types. Application to synthesized data shows that the spatial distributions of three proteins can be reliably estimated. Application to experimental data provides the first maps of the spatial relationship between groups of three different signaling molecules. The work is an important step toward a more complete understanding of membrane spatial organization and dynamics during signaling.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Therefore, approximation techniques such as the mean field approximation (Celeux et al, 2003;Tonazzini et al, 2006;Zhang, 1992) and pseudo-likelihood method (Chalmond, 1989;Zhang et al, 1994) are used. The EM algorithm has been extended for parameter estimation on a quadtree (Bouman and Shapiro, 1994;Laferté et al, 2000). …”

Section: Related Workmentioning

confidence: 99%

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Zhang¹

2007

Bull. Math. Biol.

View full text Add to dashboard Cite

show abstract

“…A particular form of such models is a causal tree in which each node has only one parent. These models have been used with some success in various segmentation and labeling problems (Bouman and Shapiro, 1994) (Cheng and Bouman, 2001) (Feng et al, 2002) (Wilson and Li, 2003) (Kumar and Hebert, 2003c).…”

Section: Causal Modelsmentioning

confidence: 99%

Discriminative Random Fields

Kumar

2014

Computer Vision

104

View full text Add to dashboard Cite

Abstract.In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by Lafferty et al (Lafferty et al., 2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results.

show abstract

“…In the graphical-models literature, the bestknown inference algorithm for TSBNs is Pearl's message passing scheme [5,18]; similar algorithms have been proposed in the image-processing literature [8,14,15]. Essentially, all these algorithms perform belief propagation up and down the tree, where after a number of training cycles, we obtain all the tree parameters necessary to compute P (X|Y ).…”

Section: Tree-structured Belief Networkmentioning

confidence: 99%

“…The most commonly used MRF model is the tree-structured belief network (TSBN) [8,9,[14][15][16]. A TSBN is a generative model comprising hidden, X, and observable, Y , random variables (RVs) organized in a tree structure.…”

Section: Tree-structured Belief Networkmentioning

confidence: 99%

Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification

Todorovic

Nechyba

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, we present a vision system for object recognition in aerial images, which enables broader mission profiles for Micro Air Vehicles (MAVs). The most important factors that inform our design choices are: real-time constraints, robustness to video noise, and complexity of object appearances. As such, we first propose the HSI color space and the Complex Wavelet Transform (CWT) as a set of sufficiently discriminating features. For each feature, we then build tree-structured belief networks (TSBNs) as our underlying statistical models of object appearances. To perform object recognition, we develop the novel multiscale Viterbi classification (MSVC) algorithm, as an improvement to multiscale Bayesian classification (MSBC). Next, we show how to globally optimize MSVC with respect to the feature set, using an adaptive feature selection algorithm. Finally, we discuss context-based object recognition, where visual contexts help to disambiguate the identity of an object despite the relative poverty of scene detail in flight images, and obviate the need for an exhaustive search of objects over various scales and locations in the image. Experimental results show that the proposed system achieves smaller classification error and fewer false positives than systems using the MSBC paradigm on challenging real-world test images.

show abstract

A multiscale random field model for Bayesian image segmentation

Cited by 525 publications

References 45 publications

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Discriminative Random Fields

Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification

Contact Info

Product

Resources

About