MRI texture analysis on texture test objects, normal brain and intracranial tumors

Texture analysis was performed in three different MRI units on T1 and T2-weighted MR images from 10 healthy volunteers and 63 patients with histologically confirmed intracranial tumors. The goal of this study was a multicenter evaluation of the usefulness of this quantitative approach for the characterization of healthy and pathologic human brain tissues (white matter, gray matter, cerebrospinal fluid, tumors and edema). Each selected brain region of interest was characterized with both its mean gray level values and several texture parameters. Multivariate statistical analyses were then applied in order to discriminate each brain tissue type represented by its own set of texture parameters. Texture analysis was previously performed on test objects to evaluate the method dependence on acquisition parameters and consequently the interest of a multicenter evaluation. Even obtained on different sites with their own acquisition routine protocol, MR brain images contain textural features that can reveal discriminant factors for tissue classification and image segmentation. It can also offer additional information in case of undetermined diagnosis or to develop a more accurate tumor grading.     

Influence of age and osteoporosis on calcaneus trabecular bone structure: a preliminary in vivo MRI study by quantitative texture analysis

Recent developments in high-resolution MR imaging techniques have opened up new perspectives for structural characterization of trabecular bone by non-invasive methods. In this study, 3-D MR imaging was performed on 17 healthy volunteers and 6 osteoporotic patients. Two different MR sequences were used to evaluate the impact on MR acquisition on texture analysis results. Images were analyzed with four automated methods of texture analysis (grey level histogram, cooccurrence, runlength and gradient matrices) enabling quantitative analysis of grey level intensity and distribution within three different regions of interest (ROI). Texture analysis is not very frequently used since the interpretation of the large number of calculated parameters is difficult. We applied multiparametric data analyses such as principal component analysis (CFA) and hierarchical ascending classification (HAC) to determine the relevant parameters to differentiate between three sets of images (healthy young volunteers, healthy postmenopaused and osteoporotic patients). The results suggest that relevant texture information (depending on the ROI localization in the calcaneus) can be extracted from calcaneus MR images to evaluate osteoporosis and age effects on trabecular bone structure if strictly the same acquisition sequences are used for all patients' examination.     

Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images?: Objective evaluation by means of texture analysis

Objectives

To objectively identify possible differences in the signal characteristics of benign and malignant soft tissue masses (STM) on magnetic resonance (MR) images by means of texture analysis and to determine the value of these differences for computer-assisted lesion classification.

Method

Fifty-eight patients with histologically proven STM (benign, n=30; malignant, n=28) were included. STM texture was analyzed on routine T1-weighted, T2-weighted and short tau inversion recovery (STIR) images obtained with heterogeneous acquisition protocols. Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC) were calculated to identify the most discriminative texture features for separation of benign and malignant STM. F>1 indicated adequate discriminative power of texture features. Based on the texture features, computer-assisted classification of the STM by means of k-nearest-neighbor (k-NN) and artificial neural network (ANN) classification was performed, and accuracy, sensitivity and specificity were calculated.

Results

Discriminative power was only adequate for two texture features, derived from the gray-level histogram of the STIR images (first and 10th gray-level percentiles). Accordingly, the best results of STM classification were achieved using texture information from STIR images, with an accuracy of 75.0% (sensitivity, 71.4%; specificity, 78.3%) for the k-NN classifier, and an accuracy of 90.5% (sensitivity, 91.1%; specificity, 90.0%) for the ANN classifier.

Conclusion

Texture analysis revealed only small differences in the signal characteristics of benign and malignant STM on routine MR images. Computer-assisted pattern recognition algorithms may aid in the characterization of STM, but more data is necessary to confirm their clinical value.     

Texture analysis of multiple sclerosis: a comparative study

The difficulty of using magnetic resonance imaging (MRI) to support early diagnosis of multiple sclerosis (MS) stems from the subtle pathological changes in the central nervous system (CNS). In this study, texture analysis was performed on MR images of MS patients and normal controls and a combined set of texture features were explored in order to better discriminate tissues between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM). Features were extracted from gradient matrix, run-length (RL) matrix, gray level co-occurrence matrix (GLCM), autoregressive (AR) model and wavelet analysis, and were selected based on greatest difference between different tissue types. The results of the combined set of texture features were compared with our previous results of GLCM-based features alone. The results of this study demonstrated that (1) with the combined set of texture features, classification was perfect (100%) between MS lesions and NAWM (or NWM), less successful (88.89%) among the three tissue types and worst (58.33%) between NAWM and NWM; (2) compared with GLCM-based features, the combined set of texture features were better at discriminating MS lesions and NWM, equally good at discriminating MS lesions and NAWM and at all three tissue types, but less effective in classification between NAWM and NWM. This study suggested that texture analysis with the combined set of texture features may be equally good or more advantageous than the commonly used GLCM-based features alone in discriminating MS lesions and NWM/NAWM and in supporting early diagnosis of MS.     

基于统计特征的轮胎纹理缺陷在线检测

根据轮胎X光纹理缺陷区域灰度及灰度分布异常的特点,研究了一种通过分析统计特征进行在线缺陷检测的方法。在轮胎X光纹理灰度分布模型基础上,采用正则化预处理去除背景噪声,然后进行图像分块,分别计算每块的灰度均值和方差,并采用双线性插值运算形成均值图像和方差图像,再通过二值化实现缺陷检测。实验表明,与人工检测方法进行对比,该方法误判率低,检测精度高,并且运算速度快,能满足在线检测要求。     

基于脑部磁共振图像三维局部模式变换特征提取进行阿尔茨海默病病程预测分类

本文提出一种三维局部模式变换提取进行纹理特征并与常规特征相融合的方法,基于脑部磁共振图像,对认知功能正常的健康人体（CN）、轻度认知障碍（MCI）患者和阿尔茨海默病（AD）患者进行预测分类.首先对46例CN对照组、61例MCI患者和25例AD患者的脑部磁共振图像提取感兴趣区域,然后提取双侧海马体组织、灰质和白质的三维局部模式变换纹理特征和常规特征,并将两类特征融合,使用支持向量机分类算法进行分类.结果显示利用本方法,基于双侧海马体组织对AD组和CN组进行分类的准确率为88.73%、敏感度为78.00%、特异度为95.7%、受试者工作特征（ROC）曲线下面积（AUC）为0.886 5;基于灰质的准确率为85.92%、敏感度为80.00%、特异度为86.6%、AUC为0.854 3.这证明基于海马体磁共振图像,利用本文提出的改进三维局部模式变换提取的纹理特征进行阿尔茨海默病病程分类效果较好,融合常规特征后更可提高分类预测的精度.     

MRI denoising using progressively distribution-based neural network

Magnetic Resonance (MR) images often suffer from noise pollution during image acquisition and transmission, which limits the accuracy of quantitative measurements from the data. Noise in magnitude MR images is usually governed by Rician distribution, due to the existence of uncorrelated Gaussian noise with zero-mean and equal variance in both the real and imaginary parts of the complex K-space data. Different from the existing MRI denoising methods that utilizing the spatial neighbor information around the pixels or patches, this work turns to capture the pixel-level distribution information by means of supervised network learning. A progressive network learning strategy is proposed via fitting the distribution of pixel-level and feature-level intensities. The proposed network consists of two residual blocks, one is used for fitting pixel domain without batch normalization layer and another one is applied for matching feature domain with batch normalization layer. Experimental results under synthetic, complex-valued and clinical MR brain images demonstrate great potential of the proposed network with substantially improved quantitative measures and visual inspections.     

Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle

Automated magnetic resonance imaging (MRI) texture analysis was compared with visual MRI analysis for the diagnosis of skeletal muscle dystrophy in 14 healthy and 17 diseased subjects. MRI texture analysis was performed on 8 muscle regions of interest (ROI) using four statistical methods (histogram, co-occurrence matrix, gradient matrix, runlength matrix) and one structural (mathematical morphology) method. Nine senior radiologists assessed full leg transverse slice images and proposed a diagnosis. The 59 extracted texture parameters for each ROI were statistically analyzed by Correspondence Factorial Analysis. Non-parametric tests were used to compare diagnoses based on automated texture analysis and visual analysis. Texture analysis methods discriminated between healthy volunteers and patients with a sensitivity of 70%, and a specificity of 86%. Comparison with visual analysis of MR images suggests that texture analysis can provide useful information contributing to the diagnosis of skeletal muscle disease.     

应用高光谱成像技术鉴别绿茶品牌研究

应用高光谱成像技术,基于光谱主成分信息和图像信息的融合实现名优绿茶不同品牌的鉴别。首先采集6个品牌名优绿茶在380～1 023 nm波长范围的512幅光谱图像,然后提取并分析绿茶样本的可见近红外光谱响应特性,结合主成分分析法找到了最能体现这6类样本差异的2个特征波段(545和611 nm),并从这2个特征波段图像中分别提取12个灰度共生矩阵纹理特征参量包括中值、协方差、同质性、能量、对比度、相关、熵、逆差距、反差、差异性、二阶距和自相关,最后融合这12个纹理特征和三个主成分特征变量得到名优绿茶品牌识别的特征信息,利用LS-SVM建立区分模型,预测集识别率达到了100%,同时采用ROC曲线的评估方法来评估分类模型。结果表明综合应用灰度共生矩阵变量和光谱主成分变量作为LS-SVM模型输入可实现对绿茶品牌的鉴别。     

纹理分析在红外目标识别中的应用

若背景有雾时，红外图像中的目标会受到影响而变得比较模糊。雾的纹理是一种比较典型的自然纹理，利用灰度共生矩阵提取纹理特征图像的方法，对雾天获取的红外图像进行分析。通过实验证明了该方法对红外图像中（背景有雾时）目标的提取和识别有积极的作用。     

Assessment of scanner performance and normalization of estimated relaxation rate values

The purpose of this study was to develop and test a method for the assessment of Magnetic Resonance (MR) scanner performance suitable for routine brain MR studies and for normalization of calculated relaxation times. We hypothesized that regular monitoring of machine performance changes could provide a helpful normalization tool for calculating tissue MR parameters, thus contributing to support their use for longitudinal and comparative studies of both normal and diseased tissues.The method is based on the acquisition of phantom images during routine brain studies with standard spin-echo sequences. MR phantom and brain tissue parameters were used to assess the influence of machine related changes on relaxation parameter estimates. Experimental results showed that scanner performance may affect relaxation rate estimates. Phantom and in vivo results indicate that the correction method yields a reduction in variability of estimated phantom R1 values up to 29% and of R1 for different brain structures up to 17%. These findings support the validity of using brain coil phantoms for routine system monitoring and correction of tissue relaxation rates.     

Multicontext wavelet-based thresholding segmentation of brain tissues in magnetic resonance images

A novel segmentation method based on wavelet transform is presented for gray matter, white matter and cerebrospinal fluid in thin-sliced single-channel brain magnetic resonance (MR) scans. On the basis of the local image model, multicontext wavelet-based thresholding segmentation (MCWT) is proposed to classify 2D MR data into tissues automatically. In MCWT, the wavelet multiscale transform of local image gray histogram is done, and the gray threshold is gradually revealed from large-scale to small-scale coefficients. Image segmentation is independently performed in each local image to calculate the degree of membership of a pixel to each tissue class. Finally, a strategy is adopted to integrate the intersected outcomes from different local images. The result of the experiment indicates that MCWT outperforms other traditional segmentation methods in classifying brain MR images.     

Improved tomographic reconstructions using adaptive time‐dependent intensity normalization

The first processing step in synchrotron‐based micro‐tomography is the normalization of the projection images against the background, also referred to as a white field. Owing to time‐dependent variations in illumination and defects in detection sensitivity, the white field is different from the projection background. In this case standard normalization methods introduce ring and wave artefacts into the resulting three‐dimensional reconstruction. In this paper the authors propose a new adaptive technique accounting for these variations and allowing one to obtain cleaner normalized data and to suppress ring and wave artefacts. The background is modelled by the product of two time‐dependent terms representing the illumination and detection stages. These terms are written as unknown functions, one scaled and shifted along a fixed direction (describing the illumination term) and one translated by an unknown two‐dimensional vector (describing the detection term). The proposed method is applied to two sets (a stem Salix variegata and a zebrafish Danio rerio) acquired at the parallel beam of the micro‐tomography station 2‐BM at the Advanced Photon Source showing significant reductions in both ring and wave artefacts. In principle the method could be used to correct for time‐dependent phenomena that affect other tomographic imaging geometries such as cone beam laboratory X‐ray computed tomography.     

Handcrafted fuzzy rules for tissue classification

This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.     

基于灰度共生矩阵的表面粗糙度研究

介绍了激光散斑的形成及其统计性质,拍摄大量不同加工工艺、不同表面粗糙度标准样块的散斑图像,基于激光散斑图像的灰度共生矩阵及惯性矩、角二阶矩、熵、相关性4个参量,对图像进行了纹理分析.分别绘制了不同加工工艺、不同表面粗糙度标准样块的4个参量与表面粗糙度关系的特性曲线.最后将实验结果和未知表面粗糙度工件的实验数据进行比对,准确估算出了工件的表面粗糙度.     

Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data

Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n > 3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen.     

Detection of late epilepsy by the texture analysis of MR brain images in the lithium-pilocarpine rat model

The aim of the study was to detect by texture analysis non easily visible anomalies of magnetic resonance (MR) images of piriform and entorhinal cortices relevant to the lithium-pilocarpine (Li-Pilo) model of temporal lobe epilepsy in rats. Status epilepticus was induced by Li-Pilo in twenty male rats 21 day-old. T₂-weighted MR images of their brain, were obtained before injection of Li-Pilo and one day after status epilepticus. An hyperintense signal was found in the piriform and entorhinal cortices of six rats, which developed chronic epilepsy after a latent period of one to three months. Among the 14 other rats which displayed images similar to those obtained before injection, four remained healthy but 10 rats developed late epileptic symptoms, raising the problem of hidden cortical damage which may be too subtle to be detected by classic MRI examination. A numeric treatment of digital images was then undertaken by texture analysis, to derive image information from a purely computational point of view. The combined texture and discriminant analyses based on pixels pattern anomalies, selected 3 texture parameters derived from co-occurrence matrix which characterized structural abnormalities relevant to the hyperintense signal, not only in the modified images of 6 rats but also in images of 10 rats with apparently non modified images. These three texture’s parameters allowed to classify the twenty rats of our experiment as follows: sixteen epileptic rats were effectively classified with cortical lesions, two non epileptic were correctly classified with healthy cortex, but two healthy rats were not correctly classified. This misclassification is discussed on the basis of the time dependence of the onset of seizure in the Li-Pilo model. These promising results suggest to apply this method to MRI examinations for an improvement of the early diagnostic of human epilepsy.     

Texture analysis of magnetic resonance images of rat muscles during atrophy and regeneration

OBJECTIVES: The goals of the current study were (i) to introduce texture analysis on magnetic resonance imaging (MRI-TA) as a noninvasive method of muscle investigation that can discriminate three muscle conditions in rats; these are normal, atrophy and regeneration; and (ii) to show consistency between MRI-TA results and histological results of muscle type 2 fibers' cross-sectional area. METHOD: Twenty-three adult female Wistar rats were randomized into (i) control (C), (ii) immobilized (I) and (iii) recovering (R) groups. For the last two groups, the right hind limb calf muscles were immobilized against the abdomen for 14 days; then, the hind limb was remobilized only for the R group for 40 days. At the end of each experimental period, MRI was performed using 7-T magnet Bruker Avance DRX 300 (Bruker, Wissembourg); T1-weighted MRI acquisition parameters were applied to show predominantly muscle fibers. Rats were sacrificed, and the gastrocnemius muscle (GM) was excised immediately after imaging. (A) Histology: GM type 2 fibers (fast twitch) were selectively stained using the adenosine triphosphatase (ATPase) technique. The mean cross-sectional areas were compared between the three groups. (B) Image analysis: regions of interest (ROIs) were selected on GM MR images where statistical methods of texture analysis were applied followed by linear discriminant analysis (LDA) and classification. RESULTS: Histological analysis showed that the fibers' mean cross-sectional areas on GM transversal sections represented a significant statistical difference between I and C rats (ANOVA, P<.001) as well as between R and I rats (ANOVA, P<.01), but not between C and R rats. Similarly, MRI-TA on GM transversal images detected different texture for each group with the highest discrimination values (Fisher F coefficient) between the C and I groups, as well as between I and R groups. The lowest discrimination values were found between C and R groups. LDA showed three texture classes schematically separated. CONCLUSION: Quantitative results of MRI-TA were statistically consistent with histology. MRI-TA can be considered as a potentially interesting, reproducible and nondestructive method for muscle examination during atrophy and regeneration.     

基于I-BGLAM纹理和光谱融合的高光谱显微成像木材树种分类

为了提高木材树种分类的正确率,提出了一种基于I-BGLAM纹理特征和光谱特征融合的高光谱图像的木材树种分类方法。实验数据是利用SOC710VP高光谱成像仪获取的可见光/近红外（372.53~1 038.57 nm）范围内的高光谱图像。首先,利用基于OIF的特征波段选择方法降低高光谱图像的维数,选择出含有信息量大的波段。其次,对选择出的波段图像使用NSCT及NSCT逆变换得到融合图像,对得到的融合图像使用I-BGLAM提取其纹理特征。与此同时,对高光谱图像的全波段求取平均光谱并进行S-G（Savitzky-Golay）平滑得到光谱特征。最后,将得到的纹理特征和光谱特征融合后送进极限学习机（ELM）中进行分类。此外,还和基于灰度共生矩阵（GLCM）的木材识别的传统方法以及近几年木材树种识别领域内被提出的主流方法进行了比较。该研究主要创新点有两个：一是将强纹理提取器I-BGLAM用于高光谱图像中提取其纹理特征;二是提出一种新的特征融合的模型用于高光谱图像的分类。针对8个树种的实验结果表明,单独使用I-BGLAM提取的纹理特征来进行分类的正确率最高可到达88.54%,而使用GLCM提取纹理特征的传统方法正确率最高只有76.04%,该结果可以得出本文使用I-BGLAM在纹理特征提取方面要优于GLCM,这为后面建立的融合模型打下很好的基础,单独使用平均光谱特征来分类的正确率最高可以达到92.71%,使用所提出的特征融合方法所得到的分类正确率最高可达到100%,这说明使用所提出的融合模型来分类要比以前单独使用某一种特征的分类模型要好。此外,使用所提出的方法得到的分类正确率要高于本领域内其他两种主流的识别方法。因此,所提出的基于I-BGLAM纹理特征和光谱特征融合的方法能够提高木材树种分类的正确率,该方法在木材树种分类方面有着一定的利用价值。     

基于分维特征和反向传播神经网络的自然纹理识别

提出一种利用分维特征, 即自然纹理的自相似性进行纹理识别的研究。利用原始图像、高灰度图像、低灰度图像、四个方向（０°, ４５°, ９０°, １３５°）的梯度图像及二阶多分维共八个分维数作为特征值; 分维的计算采用改进的盒子计数法（ＭＢＣＭ）; 最后利用反向传播（ＢＰ）神经网络进行纹理的分类识别。实验结果与其它技术进行了比较, 并提出利用维纳滤波进一步改进分类性能。