Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy

White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.     

Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images

Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk of developing cognitive decline. However, their actual role in the conversion to dementia is still not fully understood. Automatic segmentation methods can help in the screening and monitoring of Mild Cognitive Impairment patients who take part in large population-based studies. Most existing segmentation approaches use multimodal MR images. However, multiple acquisitions represent a limitation in terms of both patient comfort and computational complexity of the algorithms. In this work, we propose an automatic lesion segmentation method that uses only three-dimensional fluid-attenuation inversion recovery (FLAIR) images. We use a modified context-sensitive Gaussian mixture model to determine voxel class probabilities, followed by correction of FLAIR artifacts. We evaluate the method against the manual segmentation performed by an experienced neuroradiologist and compare the results with other unimodal segmentation approaches. Finally, we apply our method to the segmentation of multiple sclerosis lesions by using a publicly available benchmark dataset. Results show a similar performance to other state-of-the-art multimodal methods, as well as to the human rater.     

Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques

White matter loss, ventricular enlargement and white matter lesions are common findings on brain scans of older subjects. Accurate assessment of these different features is therefore essential for normal aging research. Recently, we developed a novel unsupervised classification method, named ‘Multispectral Coloring Modulation and Variance Identification’ (MCMxxxVI), that fuses two different structural magnetic resonance imaging (MRI) sequences in red/green color space and uses Minimum Variance Quantization (MVQ) as the clustering technique to segment different tissue types. Here we investigate how this method performs compared with several commonly used supervised image classifiers in segmenting normal-appearing white matter, white matter lesions and cerebrospinal fluid in the brains of 20 older subjects with a wide range of white matter lesion load and brain atrophy. The three tissue classes were segmented from T₁-, T₂-, T₂?- and fluid attenuation inversion recovery (FLAIR)-weighted structural MRI data using MCMxxxVI and the four supervised multispectral classifiers available in the Analyze package, namely, Back-Propagated Neural Networks, Gaussian classifier, Nearest Neighbor and Parzen Windows. Bland–Altman analysis and Jaccard index values indicated that, in general, MCMxxxVI performed better than the supervised multispectral classifiers in identifying the three tissue classes, although final manual editing was still required to deliver radiologically acceptable results. These analyses show that MVQ, as implemented in MCMxxxVI, has the potential to provide quick and accurate white matter segmentations in the aging brain, although further methodological developments are still required to automate fully this technique.     

Detecting cortical lesions in multiple sclerosis at 7 T using white matter signal attenuation

Cortical lesions have recently been a focus of multiple sclerosis (MS) MR research. In this study, we present a white matter signal attenuating sequence optimized for cortical lesion detection at 7 T. The feasibility of white matter attenuation (WHAT) for cortical lesion detection was determined by scanning eight patients (four relapsing/remitting MS, four secondary progressive MS) at 7 T. WHAT showed excellent gray matter-white matter contrast, and cortical lesions were hyperintense to the surrounding cortical gray matter, The sequence was then optimized for cortical lesion detection by determining the set of sequence parameters that produced the best gray matter-cortical lesion contrast in a 10-min scan. Despite the B1 inhomogeneities common at ultra-high field strengths, WHAT with an adiabatic inversion pulse showed good cortical lesion detection and would be a valuable component of clinical MS imaging protocols.     

Quantitative estimation of brain white matter abnormalities in elderly subjects using magnetic resonance imaging

Twenty-five elderly subjects were examined with brain magnetic resonance imaging (MRI). The subjects were divided into two groups: those with Mini-Mental State Examination (MMSE) scores above 25, and those subjects with MMSE scores between 18 and 24. The degree of white matter abnormalities (WMA) (expressed as relative volumes) as well as the presence of cerebrovascular risk factors were evaluated in the two groups. We found that a) subjects with low MMSE scores had significantly larger relative volumes of WMA than the subjects with higher scores, b) a significant correlation (r_s = 0.53, p < 0.009) between MMSE scores and the relative volume of WMA was also established, and c) a weak significant correlation (r_s = −0.51, p < 0.05) between arterial blood pressure and WMA was found in the subjects with high MMSE scores. Besides these findings no other correlations between the presence of cerebrovascular risk factors and WMA were found in any of the groups.     

一种基于虚拟传感的无需误差传声器的自适应有源降噪方法*

该文基于虚拟传感技术引入了一种用于耳机的无需误差传声器的自适应有源降噪方法。该算法仅使用一个参考传声器实现了一种前馈和反馈自适应算法结合的有源降噪算法,提高了有源降噪稳定性,简化了耳机硬件结构。利用DSP平台实现了该文提出的方案,并通过实验验证了其良好的降噪性能和实用价值。