A dynamic texture based segmentation method for ultrasound images with Surfacelet, HMT and parallel computing

Dynamic texture based smoke detection using Surfacelet transform and HMT model

Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (e.g., using the expectation-maximization algorithm). We greatly simplify the HMT model by exploiting the inherent self-similarity of real-world images. The simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that fixes these nine parameters. The uHMT requires no training of any kind, while extremely simple, we show using a series of image estimation/denoising experiments that these new models retain nearly all of the key image structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both visually and in mean square error.

The authors propose a multiscale Bayesian texture segmentation algorithm that is based on a complex wavelet domain hidden Markov tree (HMT) model and a hybrid label tree (HLT) model. The HMT model is used to characterise the statistics of the magnitudes of complex wavelet coefficients. The HLT model is used to fuse the interscale and intrascale context information. In the HLT, the interscale information is fused according to the label transition probability directly resolved by an EM algorithm. The intrascale context information is also fused so as to smooth out the variations in the homogeneous regions. In addition, the statistical model at pixel-level resolution is formulated by a Gaussian mixture model (GMM) in the complex wavelet domain at scale 1, which can improve the accuracy of the pixel-level model. The experimental results on several texture images are used to evaluate the algorithm.

The wavelet-domain hidden Markov tree (HMT) model provides a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. However, it is usually assumed that the subbands at the same level are independent in the traditional HMT model. This paper proposes a modified HMT model, SHMT-S, in which a vector constructed from the coefficients at the same location of the subbands of the same level, is controlled by a hidden state. Meanwhile we also use the vector Laplace mixture distributions to fit the wavelet coefficients vector, which is peakier in the center and has heavier tails compared with Gaussian distribution. By using the HMT segmentation framework, we develop SHMT-S based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the proposed method.

In this paper, we present a framework for functional MR image restoration based on the Hidden Markov Tree (HMT) model. Under this scheme, the wavelet/contourlet coefficients of the distorted image are filtered using the HMT model of the baseline image to minimise the statistical divergence between two images. An iterative algorithm between image registration and HMT filtering is developed to achieve a trade-off between the least mean square error (in the spatial domain) and the minimum statistical divergence (in the spectral domain). We demonstrate that the proposed method can eliminate the motion artefacts (such as spikes and burring) in the Functional MR Imaging data more effectively, leading to reliable neural activity detection. This method can also be used for image restoration in other medical imaging applications.

The uniform discrete curvelet transform (UDCT) is a variant of the discrete curvelet transform with basis function located on a uniform grid. The transform is implemented based on a filter bank structure in the frequency domain. In this paper, we study statistics and dependency analysis of the UDCT coefficients and impose the hidden Markov tree (HMT) model for the UDCT. Finally, the experiment in image denoising is shown as an application of the modeling.

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelet-domain hidden Markov tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of wavelet coefficients. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides provides a good classifier for textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform multiscale texture classification at various scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain a reliable final segmentation. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images, without the need for decompression. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.

A semi-supervised hidden Markov tree (HMT) model is developed for texture analysis, incorporating both labeled and unlabeled data for training; the optimal balance between labeled and unlabeled data is estimated via the homotopy method. In traditional EM-based semi-supervised modeling, this balance is dictated by the relative size of labeled and unlabeled data, often leading to poor performance. Semi-supervised modeling may be viewed as a source allocation problem between labeled and unlabeled data, controlled by a parameter lambda isin [0,1], where lambda = 0 and 1 correspond to the purely supervised HMT model and purely unsupervised HMT-based clustering, respectively. We consider the homotopy method to track a path of fixed points starting from lambda = 0, with the optimal source allocation identified as a critical transition point where the solution is unsupported by the initial labeled data. Experimental results on real textures demonstrate the superiority of this method compared to the EM-based semi-supervised HMT training

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we propose a contourlet-based despeckling method for the SAR image using the hidden Markov tree (HMT) and Gaussian Markov models. The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. The HMT and Gaussian Markov models will reflect the correlations of the contourlet coefficients not only across scales and directions but also between neighbors. The experimental results show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect.

Multiresolution signal and image models such as the hidden Markov tree (HMT) aim to capture the statistical structures of smooth and singular (textured and edgy) regions. Unfortunately, models based on the orthogonal wavelet transform suffer from shift-variance, making them less accurate and realistic. We extend the HMT modeling framework to the complex wavelet transform, which features near shift-invariance and improved angular resolution compared to the standard wavelet transform. The model is computationally efficient (featuring linear-time computation and processing algorithms) and applicable to general Bayesian inference problems as a prior density for the data. We develop a simple multiscale maximum likelihood classification scheme based on the complex wavelet HMT that outperforms methods based on real-valued wavelet HMTs. The resulting classifier can be used as a front end in a more sophisticated multiscale segmentation algorithm.

The contourlet transform is a new extension to the wavelet transform in two dimensions using non-separable and directional filter banks. The contourlet expansion is composed of basis images oriented at varying directions in multiple scales, with flexible aspect ratios. With this rich set of basis images, the contourlet transform can effectively capture the smooth contours that are the dominant features in natural images with only a small number of coefficients. We begin with a detail study of the statistics of the contourlet coefficients of natural images, using histogram estimates of the marginal and joint distributions, and mutual information measurements to characterize the dependencies between coefficients. The study reveals the non-Gaussian marginal statistics and strong intra-subband, cross-scale, and cross-orientation dependencies of contourlet coefficients. It is also found that conditioned on the magnitudes of their generalized neighborhood coefficients, contourlet coefficients can approximately be modeled as Gaussian variables with variances directly related to the generalized neighborhood magnitudes. Based on these statistics, we model contourlet coefficients using a hidden Markov tree (HMT) model that can capture all of their inter-scale, inter-orientation, and intra-subband dependencies. We experiment this model in the image denoising and texture retrieval applications where the results are very promising. In denoising, contourlet HMT outperforms wavelet HMT and other classical methods in terms of both peak signal-to-noise ratio (PSNR) and visual quality. In particular, it preserves edges and oriented features better than other existing methods. In texture retrieval, it shows improvements in performance over wavelet methods for various oriented textures.

In this paper we propose an image super-resolution algorithm using The Morphological Component Analysis(MCA) and wavelet-domain Hidden Markov Tree(HMT) model. The MCA is a useful method for signal decomposing, using proper basis, we could separate features contained in a signal when these features present different morphological aspects. Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients. In this paper, we first decompose an image into texture and piecewise smooth (cartoon) parts, then enlarge the cartoon part with interpolation, because wavelet-domain HMT accurately characterizes the statistics of real-world images, we specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem to acquire the enlarged texture part, finally we get a fine result.

Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images

A novel SAR image denoising scheme based on hidden Markov tree (HMT) in the quad-tree complex wavelet packet transform (QCWPT) domain was presented to achieve the tradeoff between details retainment and noise removal. A neighborhood coefficient differential window was used to compute intra-scale correlations of complex wavelet coefficients in high frequency detail subimage, and intra-scale correlational state was identified according to the smallest error rate Bayesian decision-making rules. A HMT was fitted to describe the correlations between the complex wavelet coefficients across decomposition scales and mark inter-scale correlational state. The product results of corresponding positional intra-scale and inter-scale correlational state were looked as a new hidden state transition probability. A set of iterative equations was developed using the expectation-maximization(EM) algorithm to estimate the model parameters and produce denoising images. Experimental results show that the proposed denoising algorithm is superior to the traditional filtering methods and possible to achieve an excellent balance between suppress speckle noise effectively and preserve as many image details and edges as possible.

ABSTRACTGeneration of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology features including morphology, stemness, and proliferative behavior. Here we use an unsupervised machine learning method for Hidden Markov Trees (HMTs), which can be applied to large datasets to classify precursors on the basis of morphology, cell‐cycle length, and behavior during mitosis. The unbiased lineage analysis automatically identifies cell types by applying a lineage‐based clustering and model‐learning algorithm to a macaque corticogenesis dataset. The algorithmic results validate previously reported observer classification of precursor types and show numerous advantages: It predicts a higher diversity of progenitors and numerous potential transitions between precursor types. The HMT model can be initialized to learn a user‐defined number of distinct classes of precursors. This makes it possible to 1) reveal as yet undetected precursor types in view of exploring the significant features of precursors with respect to specific cellular processes; and 2) explore specific lineage features. For example, most precursors in the experimental dataset exhibit bidirectional transitions. Constraining the directionality in the HMT model leads to a reduction in precursor diversity following multiple divisions, thereby suggesting that one impact of bidirectionality in corticogenesis is to maintain precursor diversity. In this way we show that unsupervised lineage analysis provides a valuable methodology for investigating fundamental features of corticogenesis. J. Comp. Neurol. 524:535–563, 2016. © 2015 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.