Differential vibratory characteristics of adductor spasmodic dysphonia and muscle tension dysphonia on high-speed digital imaging.

OBJECTIVES: The purpose of this study was to quantify disorder-specific signature kinematic disturbances of vibratory motion in adductor spasmodic dysphonia (AdSD) and muscle tension dysphonia (MTD), in voice disturbances of a severe nature, with the use of high-speed digital imaging (HSDI). A secondary hypothesis of the study was to investigate the sensitivity and specificity of the signature kinematic features obtained from HSDI, in differentiating between AdSD and MTD. METHODS: We used vibratory features from automated extraction of vocal fold motion waveforms and glottal cycle montage analysis from HSDI for differential kinematic profiling of AdSD and MTD. RESULTS: Novel features of motion irregularities and micromotions (as small as 27 ms) were greater in number for AdSD, whereas reduced motion irregularities, absence of oscillatory breaks, absence of micromotions, and increased hyperfunction characterized the MTD group. Oscillatory breaks (as small as 8 ms), although present only in the AdSD group, were not statistically significant because of their reduced number of occurrences compared to the other features. Further montage analysis of successive glottal cycles of oscillatory breaks in the AdSD group revealed 3 different kinematic patterns within the AdSD group, indicative of likely AdSD with: 1) possible predominant thyroarytenoid muscle involvement, 2) possible predominant cricothyroid muscle involvement, and 3) possible combined involvements of the thyroarytenoid and lateral cricoarytenoid muscles. Four consistent but unique kinematic patterns were identified within the MTD group: 1) diplophonia, 2) vocal fry, 3) breathy phonation, and 4) pressed phonation. Sensitivity and specificity analysis revealed that only motion irregularity was a significant predictor of the presence of AdSD. CONCLUSIONS: Fine kinematic analysis from HSDI can be used to aid detailed clinical profiling of the source characteristics of AdSD and MTD.

A Bayesian framework for image segmentation with spatially varying mixtures.

A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise.

Maximum a posteriori video super-resolution using a new multichannel image prior.

Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new multichannel image prior model, along with the state-of-the-art single channel image prior and observation models. A hierarchical (two-level) Gaussian nonstationary version of the multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted multichannel approach.

Variational bayesian image restoration with a product of spatially weighted total variation image priors.

In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.

Sparse Bayesian modeling with adaptive kernel learning.

Sparse kernel methods are very efficient in solving regression and classification problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure. In this paper, we propose an incremental method for supervised learning, which is similar to the relevance vector machine (RVM) but also learns the parameters of the kernels during model training. Specifically, we learn different parameter values for each kernel, resulting in a very flexible model. In order to avoid overfitting, we use a sparsity enforcing prior that controls the effective number of parameters of the model. We present experimental results on artificial data to demonstrate the advantages of the proposed method and we provide a comparison with the typical RVM on several commonly used regression and classification data sets.

Variational Bayesian sparse kernel-based blind image deconvolution with Student's-t priors.

In this paper, we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelty of this model is the use of a sparse kernel-based model for the point spread function (PSF) that allows estimation of both PSF shape and support. In the herein proposed approach, a robust model of the BID errors and an image prior that preserves edges of the reconstructed image are also used. Sparseness, robustness, and preservation of edges are achieved by using priors that are based on the Student's-t probability density function (PDF). This pdf, in addition to having heavy tails, is closely related to the Gaussian and, thus, yields tractable inference algorithms. The approximate variational inference methodology is used to solve the corresponding Bayesian model. Numerical experiments are presented that compare this BID methodology to previous ones using both simulated and real data.

Variational Bayesian image restoration based on a product of t-distributions image prior.

Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the image restoration problem that bypasses this difficulty. An image prior is defined by imposing Student-t densities on the outputs of local convolutional filters. A variational methodology, with a constrained expectation step, is used to infer the restored image. Numerical experiments are shown that compare this methodology to previous ones and demonstrate its advantages.

Super-resolution based on fast registration and maximum a posteriori reconstruction.

In this paper, we propose a maximum a posteriori ramework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.

A class-adaptive spatially variant mixture model for image segmentation.

We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.

Non-arteritic anterior ischaemic optic neuropathy: evaluation of the brain and optic pathway by conventional MRI and magnetisation transfer imaging.

The purpose of the study was to examine the brain and the visual pathway of patients with non-arteritic anterior ischaemic optic neuropathy (NAION) by using conventional MRI (cMRI) and volumetric magnetisation transfer imaging (MTI). Thirty NAION patients, aged 67.5 +/- 8.14 years, and 28 age- and gender-matched controls were studied. MTI was used to measure the magnetisation transfer ratio (MTR) of the chiasm and for MTR histograms of the brain. The presence of areas of white matter hyperintensity (WMH) was evaluated on fluid-attenuated inversion recovery (FLAIR) images. Area of the optic nerves (ONs) and volume of the chiasm were assessed, as were coronal short-tau inversion recovery (STIR) and MTI images, respectively. More areas of WMH were observed in patients (total 419; mean 14.4; SD 19) than in controls (total 127; mean 4.7; SD 5.7), P < 0.001. Area (in square millimetres) of the affected ONs, volume(in cubic millimetres) and MTR (in percent) of the chiasm (10.7 +/- 4.6), (75.8 +/- 20.2), (56.4 +/- 6.5), respectively, were lower in patients than in controls (13.6 +/- 4.3), (158.2 +/- 75.3) (62.1 +/- 6.2), respectively, P < 0.05. Mean MTR of brain histograms was lower in patients (53.0 +/- 8.0) than in controls (58.0 +/- 5.6), P < 0.05. NAION is characterised by decreased ON and chiasmatic size. The low MTR of the chiasm and brain associated with increased areas of WMH may be suggestive of demyelination and axonal damage due to generalised cerebral vascular disease.

Bayesian restoration using a new nonstationary edge-preserving image prior.

In this paper, we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has the following two desirable features. First, it models the local image discontinuities in different directions with a model which is continuous valued. Thus, it preserves edges and generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov random fields (MRFs). Second, it is Gaussian in nature and provides estimates that are easy to compute. Using this new hierarchical prior, two restoration algorithms are derived. The first is based on the maximum a posteriori principle and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF-based with line process algorithms. These experiments demonstrate the advantages of the proposed prior.