Predicting vulnerability to sleep deprivation using diffusion model parameters.

We used diffusion modelling to predict vulnerability to decline in psychomotor vigilance task (PVT) performance following a night of total sleep deprivation (SD). A total of 135 healthy young adults (69 women, age = 21.9 ± 1.

New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance.

Context: Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.

Objectives: To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.

Design And Participants: Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2).

Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly.

Unlabelled: The link between central adiposity and cognition has been established by indirect measures such as body mass index (BMI) or waist-hip ratio. Magnetic resonance imaging (MRI) quantification of central abdominal fat has been linked to elevated risk of cardiovascular and cerebro-vascular disease. However it is not known how quantification of visceral fat correlates with cognitive performance and measures of brain structure.

Component analysis approach to estimation of tissue intensity distributions of 3D images.

Many segmentation algorithms in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a Gaussian model and iterative local optimization used to estimate the model parameters. It also does not take advantage of substantially larger amount of data provided by 3D acquisitions, which are becoming standard in clinical environment.

Interactive surface-guided segmentation of brain MRI data.

MRI segmentation is a process of deriving semantic information from volume data. For brain MRI data, segmentation is initially performed at a voxel level and then continued at a brain surface level by generating its approximation. While successful most of the time, automated brain segmentation may leave errors which have to be removed interactively by editing individual 2D slices.

Sleep deprivation and its effects on object-selective attention.

Sleep deprivation (SD) affects attention but it is an open question as to whether all subtypes of attention are similarly affected. We investigated the effects of 24 h of total SD on object-selective attention. 26 healthy, young adults viewed quartets of alternating faces or place scenes and performed selective judgments on faces only, scenes only or both faces and scenes.

Skull stripping using graph cuts.

Removal of non-brain tissues, particularly dura, is an important step in enabling accurate measurement of brain structures. Many popular methods rely on iterative surface deformation to fit the brain boundary and tend to leave residual dura. Similar to other approaches, the method proposed here uses intensity thresholding followed by removal of narrow connections to obtain a brain mask.

Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3.

Smoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J.

Evaluation of performance metrics for bias field correction in MR brain images.

Purpose: To investigate inconsistencies between common performance measures for bias field correction reported in several recent studies and propose a solution.

Materials And Methods: A set of synthetic images of a normal brain from the Montréal Simulated Brain Database (SBD) was processed using two bias field correction algorithms. The parameters of these algorithms were varied and the resulting outputs were assessed using several performance measures.

Lapsing during sleep deprivation is associated with distributed changes in brain activation.

Lapses of attention manifest as delayed behavioral responses to salient stimuli. Although they can occur even after a normal night's sleep, they are longer in duration and more frequent after sleep deprivation (SD). To identify changes in task-associated brain activation associated with lapses during SD, we performed functional magnetic resonance imaging during a visual, selective attention task and analyzed the correct responses in a trial-by-trial manner modeling the effects of response time.

Adaptive filtering for color filter array demosaicking.

Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality.

Reversing demosaicking and compression in color filter array image processing: performance analysis and modeling.

In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios.

Edge-preserving image denoising via optimal color space projection.

Denoising of color images can be done on each color component independently. Recent work has shown that exploiting strong correlation between high-frequency content of different color components can improve the denoising performance. We show that for typical color images high correlation also means similarity, and propose to exploit this strong intercolor dependency using an optimal luminance/color-difference space projection.