Analysis and synthesis of three-dimensional Gaussian Markov random fields

Ranguelova, Elena; Quinn, Anthony

doi:10.1109/icip.1999.817150

Cited by 10 publications

(14 citation statements)

References 6 publications

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“…The advantage of the conventional 3-D GMRF-based approach over 2-D GMRF-based independent slice segmentation has been shown in [3]. The method, however, is not robust against violation of the texture stationarity assumption caused by shape evolution of textured object cross-sections along the third dimension.…”

Section: Difference-field Compensationmentioning

confidence: 98%

“…This method will be refereed to as the -3D non-compensated or conventional segmentation. computationally-effective algorithm such as the Recursive Unanimity Rule (RUR) [3]. RUR allows significant accelerations to be achieved in homogeneous regions with the calculations concentrated recursively in the regions of inter-texture boundaries.…”

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

“…A 3-D MRF is a set of random variables Ø Ø ¾ ª Ø ¾ ¼ ¾ which represent the gray-level intensities at the points (voxels) of ª. The MRF is defined in respect to a Õ-th order symmetric neighbourhood geometry Õ , which is determined by the 3-D Maximum Allowable Square (MAS) rule [3]. The random variables satisfy the Markov property, i.e.…”

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

“…The model parameters can be estimated by solving the system of standard normal equations of form (3), written for each voxel within a rectangular parallelepiped-shaped computational volume Ø , centered on Ø (see Fig. 1 [3]. This method will be refereed to as the -3D non-compensated or conventional segmentation.…”

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

“…The 3-D Gaussian Markov Random Fields (GMRFs) has proven a useful model for 3-D textured data [3]. An algorithm we proposed uses a computational volume-based parameter estimation of the model at selected lattice points assuming texture stationarity.…”

Section: Introductionmentioning

confidence: 99%

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Difference Field Estimation for Enhanced 3-D Texture Segmentation

Ranguelova¹,

Quinn²

2002

Procedings of the British Machine Vision Conference 2002

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The optimization problem of finding the best match for a thin-plate block of multi-texture 3-D data in a supervised framework is studied in this paper. The textures are modelled as realizations of Gaussian Markov Random Fields (GMRFs)on 3-D lattices. The classification of the central point of the data block is performed by calculating the class probability mass function (p.m.f.s) for the block given the different texture models. The KullbackLeibler measure is proposed for the minimization of the difference between the p.m.f.s distances of the The three-dimensional (3-D) segmentation of volumetric imagery poses the challenge of estimation and compensation for the existing inter-slice difference within a multi-texture 3-D data. In this paper we propose a novel method to identify the difference field by Kullback-Leibler minimization of the distance between the class probability mass functions (p.m.f.s), calculated at thin-plate 3-D blocks of data, centered at the points of interest. and fast FFT-based technique is presented for calculation of the probability density function (p.d.f.) of the data given the model. This facilitates the calculation of the classification p.m.f.s. in a supervised framework.The estimated difference field is used to enhance the performance of a computational-volume based 3-D GMRF segmentation algorithm. The performance of the overall method is illustrated with a simulation study of mosaic of synthetic 3-D textures and MRI images of human brain.

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Section: Difference-field Compensationmentioning

confidence: 98%

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

Section: -D Gmrf-based Texture Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Difference Field Estimation for Enhanced 3-D Texture Segmentation

Ranguelova¹,

Quinn²

2002

Procedings of the British Machine Vision Conference 2002

Self Cite

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Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Foncubierta–Rodríguez

Müller

Depeursinge

2013

Multimed Tools Appl

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Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higherdimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications.

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Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

207

146

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a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

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Analysis and synthesis of three-dimensional Gaussian Markov random fields

Cited by 10 publications

References 6 publications

Difference Field Estimation for Enhanced 3-D Texture Segmentation

Difference Field Estimation for Enhanced 3-D Texture Segmentation

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

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