高分辨率外周定量计算机断层扫描评估骨小梁微结构和骨强度的研究进展

骨小梁微结构是决定骨强度及其生理功能的重要因素,而普通X线与计算机断层扫描(CT)检查不能精确反映骨小梁的真实微结构。高分辨率外周定量计算机断层扫描(HR-p QCT)是近年来新兴的一项影像学检测技术,能够定性、定量测量体内骨小梁三维微结构和体积骨矿物质密度,具有极高的精度和相对低剂量的辐射。这种新型成像工具有利于我们更加深入地认识骨小梁微结构,利用HR-p QCT数据进行有限元分析建模计算,能够准确预测骨强度,结合三维重建图像及骨小梁微结构参数还能够评估骨质疏松和骨折风险。在本综述中,我们总结了HR-p QCT的技术流程、数据参数及其临床应用等内容,以期为HR-p QCT的普及和广泛应用提供一定参考。     

基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取

目的乳腺癌的早期发现对患者意义重大。为帮助医生进行乳腺癌的早期检查和诊断,本文提出利用小波分析与图像纹理特征提取相结合的方法来提取乳腺X线图像微钙化点区域,在提高检查准确性的同时避免漏检误检。方法首先利用灰度共生矩阵所提取的能量、熵、对比度、相关性以及小波分解后得到的各层高频系数的方差、能量作为图像的特征向量,然后利用支持向量机进行训练建立最优分类模型。最后利用建立的最优分类模型实现乳腺X线图像微钙化点区域的提取并利用检出率和误检率对结果进行评估。结果使用临床数据进行验证,结果表明利用小波分析与图像纹理特征提取相结合的方法能有效提取乳腺图像中的微钙化点区域。结论基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取方法比单一的图像纹理特征提取或小波分析等方法,提取的效果更好。另外,该方法设计简单,更易于实现乳腺癌的自动化诊断。     

骨小梁Micro-CT图像形态计量学参数计算方法综述

骨质疏松症是骨科领域的研究热点之一,研究发现,除骨密度(骨量)改变的因素外,骨小梁的结构变化也是患病的重要影响因素。骨小梁形态计量学分析是研究骨小梁结构形态变化的一种重要方法。基于骨小梁的Micro-CT图像介绍部分骨小梁形态学参数的计算方法,主要包括骨小梁各向异性、连通性、结构模型指数以及纹理等不同特征,同时列举部分相关研究实例,总结上述形态学参数的适用性以及优缺点,为更加有效地评价骨质疏松状态以及药物治疗效果提供依据。     

一种基于语义模型的乳腺钙化病灶标注方法

为了实现对乳腺X线影像的医学语义标注,提出一种利用贝叶斯网络(BN)的多层乳腺影像钙化点语义建模方法。该方法首先用支持向量机(SVM)得到从图像底层视觉特征到中层特征语义的映射,然后再利用BN融合特征语义,最终提取出高层病症语义即恶性程度的概率表达,完成语义模型。将模型应用于乳腺图像的语义标注,本实验选用142幅图像作为训练集,50幅图像作为测试集,结果表明,样本标注诊断语义的准确率:恶性为81.48%,良性为73.91%。     

基于Micro-CT的小鼠骨小梁LBF模型提取及特征分析

针对小鼠股骨Micro-CT切片图像,提出一种骨小梁形态与分布特征的定量分析方法。利用形态学与LBF模型结合方法提取股骨中的骨小梁,通过模拟骨质疏松症状与提取后的正常骨小梁进行几何形态参数测量的对比,同时利用灰度共生矩阵分析骨小梁的纹理分布特征参数进行实验对比。实验结果表明,骨小梁变细、断裂以及消失等状态,其对比度、熵、能量、相关性等参数会发生明显变化,为定量化分析骨质疏松提供了一种研究方法。     

胰腺内镜超声图像纹理特征提取与分类研究

提出了胰腺内镜超声图像的纹理特征提取与分类方法,可应用于胰腺癌内镜超声图像的计算机辅助诊断.对胰腺内镜超声图像采用数字图像处理算法提取9大类共69个纹理特征.使用类间距作为可分性判据,实现特征的初步筛选,之后使用顺序前进搜索着法进一步筛选特征,并由支撑向量机实现分类.对216例病例随机选取训练集和测试集,通过多次随机实验表明,本文提出的算法实现了较高的分类准确率,为胰腺癌的临床诊断提供有价值的参考意见.     

乳腺肿块多级分形特征提取方法研究

乳腺肿块是女性的常发病,严重影响着女性健康。准确检测及定位乳腺图像中的肿块将大大提高乳腺疾病诊断的准确率。研究表明,肿块的组织结构、表面粗糙度等构成了肿块图像的纹理特征,是判别肿块的重要依据。本文提出了一种乳腺肿块多级分形特征提取方法,通过对可疑病变区域建立分形特征向量,实现了乳腺图像中腺体和肿块部分的特征提取及分析。结合支持向量机(SVM)分类方法,得出最终的诊断结果。对110幅乳腺图像进行分形特征向量提取和分类,肿块准确率达到90%。实验结果表明,本文提出的多级分形特征提取及判别方法能够有效提高乳腺肿块诊断的准确率,对乳腺肿块的早期诊断具有良好的效果。     

基于Local Jet变换空间纹理特征的肺结节分类方法

为了在纹理特征下改善肺结节良、恶性的模式识别,提出一种基于local jet变换空间的纹理特征提取方法。首先利用二维高斯函数的前三阶偏微分函数将结节原图像变换到local jet纹理图像空间,然后利用纹理描述子在该空间提取特征参数。以灰度值的前四阶矩和基于灰度共生矩阵的特征参数作为纹理描述子,分别提取结节原图像和变换后纹理图像的特征参数,以BP神经网络作为分类器,对同一纹理描述子下的2个不同图像空间的经核主成分分析优化后的特征参数集进行结节良、恶性分类。以157个肺结节(51个良性,106个恶性)作为实验数据进行对比实验,结果显示:两种纹理描述子基于local jet变换空间提取的特征参数分别获得82.69%和86.54%的分类正确率,较原图像空间提高6%～8%,同时AUC值提高约10%。实验结果表明,基于local jet变换空间提取的纹理特征可以有效地改善肺结节良、恶性的模式识别。     

HCT图像中骨小梁结构的三维重建

本文研究人体CT图像中腰椎松质骨骨小梁数据集的获取、分割及三维重建的技术方法.利用高速螺旋CT(HCT)技术和图像数码转换技术,获取了人体腰椎松质骨CT连续图像数据集,将切片图像输入二维图像处理软件进行分割,提取感兴趣区域后输入三维重建软件进行三维重建与定性分析.重建后的松质骨三维立体图像呈均匀、致密的立体网状结构,骨小梁连接清晰可见.提示利用现有软件和技术可重建松质骨骨小梁三维立体图像和诊断骨质疏松.     

基于Gabor小波和深度置信网络的肺结节良恶性分类研究

目的从频率域角度研究孤立性肺结节纹理特征,探讨深度置信网络对其良恶性的分类效果,达到辅助医生提高早期肺癌诊断准确率的目的。方法首先,利用Gabor小波对1012例患者的1072张孤立性肺结节CT图像提取纹理特征,用受限玻尔兹曼机对特征向量进行编码,学习数据本质特征;然后,用得到的纹理特征向量集训练深度置信网络,构建分类模型;最后,通过K折交叉验证法从准确性、ROC曲线下面积(AUC值)以及时间成本方面对本文提出的研究方法进行评估。结果经Gabor小波变换并构建DBN分类模型的准确度为83.75%,测试集的AUC值为0.78。与传统支持向量机分类模型相比,所提方法的准确度上升了0.56%,时间成本缩减了一半。结论利用Gabor小波从频率域提取纹理特征,结合深度置信网络构建分类模型能够取得较好的分类效果,一定程度上能够为临床诊断肺结节的良恶性提供参考。     

Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis

The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median thresholding and local adaptive thresholding (using a 7 x 7 window) successfully segmented the grey-scale images. Lacunarity analysis indicated a multifractal nature to the images, and a range of marrow space sizes with significant structure around 14-18 mm(2). Preliminary studies of in vivo images from a clinical CT scanner indicate that lacunarity analysis can follow the pattern of bone loss in osteoporosis by monitoring the homogeneity of the marrow spaces, which is related to the connectivity of the trabecular bone network and the marrow space sizes. Although the patient sample was small, derived parameters such as the maximum deviation of the lacunarity from a neutral (fractal) model, and the maximum derivative of this deviation, seem to be sufficiently sensitive to distinguish a range of bone conditions. Our results suggest that these parameters, used with bone mineral density values, may have diagnostic value in characterizing osteoporosis and predicting fracture risk.     

Applications of micro-CT and MR microscopy to study pre-clinical models of osteoporosis and osteoarthritis.

There is a tremendous unmet therapeutic need for the treatment of osteoporosis and osteoarthritis. The ovariectomized rat and the guinea pig are widely used animal models for the evaluation of new therapeutics for osteoporosis and osteoarthritis, respectively. We have utilized X-ray micro-CT techniques to quantitatively evaluate the differences in trabecular bone in the rat proximal tibia following ovariectomy and treatment with estrogen (17-B-estradiol). Results demonstrate a loss of trabecular bone and architecture following ovariectomy (p < 0.001), and a marked inhibition of trabecular bone loss in the estrogen treated group (p < 0.001). A similar change in architecture can be visualized in images obtained by high resolution MR microscopy. In addition, a good correlation was observed between the values of trabecular bone fraction (BV/TV) in the rat tibiae as obtained from 3-dimensional micro-CT data and 2-dimensional static histomorphometry (r = 0.89, 0.73, 0.79 for sham, OVX, and treated groups, respectively). Micro-CT images were also obtained from a set of lumbar vertebrae from sham operated and ovariectomized rats. Significant bone loss can be measured as early as 8 weeks following ovariectomy (p < 0.005). Micro-CT and MR images were also obtained to study age related changes in the stifle joint of the guinea pig. Significant boney changes can be seen in the tibia and femur from the animals at various ages. Changes in cartilage and joint space can also be visualized in the images. The utility of micro-CT imaging in evaluating the mouse skeletal system is illustrated by obtaining morphological and architectural details from high resolution images of the mouse hind limb and proximal tibia, respectively. The results demonstrate the advantages that multi-dimensional imaging techniques can offer in evaluating bone and joint related changes in animal models of osteoporosis and osteoarthritis.     

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians’ subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians’ subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians’ subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.     

Comparison of radiographic texture analysis from computed radiography and bone densitometry systems

Osteoporosis is a disease that results in an increased risk of bone fracture due to a loss of bone mass and deterioration of bone structure. Bone mineral density (BMD) provides a measure of bone mass and is frequently measured by bone densitometry systems to diagnose osteoporosis. In addition, computerized radiographic texture analysis (RTA) is currently being investigated as a measure of bone structure and as an additional diagnostic predictor of osteoporosis. In this study, we assessed the ability of a peripheral bone densitometry (PD) system to yield images useful for RTA. The benefit of such a system is that it measures BMD by dual-energy x-ray absorptiometry and therefore provides high- and low-energy digital radiographic images. The bone densitometry system investigated was the GE/Lunar PIXI, which provides 512 x 512 digital images of the heel or forearm (0.2 mm pixels). We compared texture features of heel images obtained with this PD system to those obtained on a Fuji computed radiography (CR) system (0.1 mm pixels). Fourier and fractal-based texture features of images from 24 subjects who had both CR and BMD exams were calculated, and correlation between the two systems was analyzed. Fourier-based texture features characterize the magnitude, frequency content, and orientation of the trabecular bone pattern. Good correlation was found between the two modalities for the first moment (FMP) with r=0.71 (p value<0.0001) and for minimum FMP with r=0.52 (p value=0.008). Root-mean-square (RMS) did not correlate with r=0.31 (p value>0.05), while the standard deviation of the RMS did correlate with r=0.79 (p value<0.0001). Good correlation was also found between the two modalities for the fractal-based texture features with r=0.79 (p value<0.0001) for the global Minkowski dimension and r=0.63 (p value=0.0007) for the fractal dimension from a box counting method. The PD system therefore may have the potential for yielding heel images suitable for RTA.     

Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines

ABSTRACT: BACKGROUND: In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. METHODS: A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naive Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. RESULTS: A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naive Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. CONCLUSIONS: A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.     

Classification of breast masses via nonlinear transformation of features based on a kernel matrix

We propose methods to perform a certain nonlinear transformation of features based on a kernel matrix, before the classification step, aiming to improve the discriminating power of the comparatively weak edge-sharpness and texture features of breast masses in mammograms, and seek better incorporation of features representing different radiological characteristics than shape features only. Kernel principal component analysis (KPCA) is applied to improve the discriminating power of each single feature in an expanded feature space and the discriminating capability of different feature combinations in other transformed, more informative, lower-dimensional feature spaces. A kernel partial least squares (KPLS) method is developed to derive score vectors for a shape feature set, and an edge-sharpness and texture feature set, respectively, with minimal covariance between each other, to help in achieving improved diagnosis using multiple radiological characteristics of breast masses. Fisher's linear discriminant analysis (FLDA) is employed to evaluate the classification capability of the transformed features. The methods were tested with a set of 57 regions in mammograms, of which 20 are related to malignant tumors and 37 to benign masses, represented using five shape features, three edge-sharpness features, and 14 texture features. The classification performance of the edge-sharpness and texture features, via KPCA transformation, was significantly improved from 0.75 to 0.85 in terms of the area under the receiver operating characteristics curve (Az). The classification performance of all of the shape, edge-sharpness, and texture features, via KPLS transformation, was improved from 0.95 to 1.0 in Az value.     

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.     

双能X射线骨密度仪不同测量方式检测大鼠骨密度的准确性

背景：双能X射线骨密度仪是诊断骨质疏松症的金标准,但采用其系统默认方式测量小动物骨密度误差很大。 目的：观察双能X射线骨密度仪不同测量方式对大鼠骨密度测量准确度的影响。 方法：应用双能X射线骨密度仪对六七月龄雌性SD大鼠进行全身扫描,分别采用自定义手动矩形方式、手动椭圆形方式与系统默认标准方式依次测量大鼠的全身、头部及脊柱部位的骨密度。 结果与结论：手动椭圆形方式与系统默认方式测得的大鼠全身、头部和脊柱的骨密度差异无显著性意义(P > 0.05),而手动矩形方式与系统默认标准方式间差异有显著性意义(P < 0.01)。双能X射线骨密度仪应用手动椭圆形方式与系统默认标准方式对测量结果影响不大,但手动矩形测量方式误差较大。提示手动椭圆形方式可作为小动物骨密度测量后的分析方法之一。