Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography

The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.     

Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography

There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists′ observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules.     

Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images

An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low-dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.     

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。     

Automated lung nodule classification following automated nodule detection on CT: a serial approach

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.     

良恶性肺小结节CT图像基于灰度共生矩阵10种纹理特征研究

目的 建立多水平模型研究良恶性肺小结节CT图像的灰度共生矩阵纹理特征,更好地描述肺小结节CT图像,达到辅助肺小结节鉴别的目的.方法 对185例2171张肺小结节CT图像基于灰度共生矩阵提取10个纹理特征,拟合多水平统计模型分析良恶性CT图像的纹理特征的差异.结果 在考虑患者水平的基础上能量、惯性矩等8个纹理特征,在良恶性肺小结节的CT图像间的差异有统计学意义.结论 基于灰度共生矩阵的一些纹理特征是反应肺小结节CT图像良恶性的有效特征参量,在一定程度上有助于早期肺癌的鉴别诊断.     

CT动态增强扫描中时间密度曲线及特征参数值对孤立性肺结节的诊断价值

目的:分析CT动态增强扫描中时间密度曲线及特征参数值对孤立性肺结节的诊断价值。方法:回顾性分析95例孤立性肺结节病变患者的临床资料,所有患者均行CT动态增强扫描检查及病理活检。依据病理结果,分析CT动态增强扫描的特异性、敏感性及准确性,并对不同类型孤立性肺结节时间密度曲线及特征参数值进行比较分析。结果:（1）95例孤立性肺结节病变患者中,恶性结节67例、良性结节14例,炎性结节14例;（2）CT动态增强扫描诊断孤立性肺结节病变性质的特异性为83.33%,敏感性为95.38%,准确性为91.58%;（3）恶性结节及炎症结节患者在CT动态增强扫描中各时间点的CT值均高于良性结节组（P<0.05）;在CT动态增强扫描300及480 s时,炎性结节组CT值较恶性结节组下降显著（P<0.05）;（4）恶性结节组及炎性结节组结节强化值（PH）、孤立性肺结节PH与主动脉PH之比均明显高于良性结节组（P<0.05）。结论:对孤立性肺结节病变患者行CT动态增强扫描,时间密度曲线及特征参数值可以为良、恶性孤立性肺结节提供一定的诊断依据。     

Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images

A novel automated computerized scheme has been developed to assist radiologists for their distinction between benign and malignant solitary pulmonary nodules on chest images. Our database consisted of 55 chest radiographs (33 primary lung cancers and 22 benign nodules). In this method, the location of a nodule was indicated first by a radiologist. The difference image with a nodule was produced by use of filters and then represented in a polar coordinate system. The nodule was segmented automatically by analysis of contour lines of the gray-level distribution based on the polar-coordinate representation. Two clinical parameters (age and sex) and 75 image features were determined from the outline, the image, and histogram analysis for inside and outside regions of the segmented nodule. Linear discriminant analysis (LDA) and knowledge about benign and malignant nodules were used to select initial feature combinations. Many combinations for subgroups of 77 features were evaluated as input to artificial neural networks (ANNs). The performance of ANNs with the selected 7 features by use of the round-robin test showed Az = 0.872, which was greater than Az = 0.854 obtained previously with the manual method (P= 0.53). The performance of LDA (Az = 0.886) was slightly improved compared to that of ANNs (P = 0.59) and was greater than that of the manual method (Az = 0.854) reported previously (P = 0.40). The high level of its performance indicates the potential usefulness of this automated computerized scheme in assisting radiologists as a second opinion for distinction between benign and malignant solitary pulmonary nodules on chest images.     

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives.The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.     

基于Curvelet变换的肺结节CT图像良恶性分类研究

目的早期肺癌患者的CT图像表现为结节状（在肺野内直径≤3cm的病灶），需要与结核球等良性病变鉴别开，以提高患者的5年生存率。方法本文基于Curvelet变换提取能量、熵、灰度均值及灰度标准差四种纹理特征值，按7：3比例将样本分为训练集与验证集。使用BP（back propagation）神经网络作为分类器。每一种纹理参数测试集的神经网络仿真值结合病理诊断结果绘制受试者工作特征曲线（receiver operator characteristic cllrve，ROC曲线），根据ROC下面积得到最优的几种纹理参数用于良恶性分类，并将分类结果与病理诊断结果进行比较。结果四种纹理参数构建的BP网络均具有诊断价值，每种纹理参数诊断价值各不相同，其中熵与灰度标准差的诊断价值优于能量与灰度均值，并且通过组合多种纹理参数可以提高诊断准确性。结论使用熵与灰度标准差两种纹理特征值构建BP神经网络能达到最好的分类效果，在一定程度上有利于肺癌的早期诊断。     

Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.     

基于肺部PET/CT图像不同纹理特征的K最近邻分类器

目的对PET/CT图像高维纹理参数进行降维,基于不同纹理参数建立肺结节良恶性的K最近邻(K-nearest neighbor,KNN)分类器,探究最佳建模方法,提高分类的准确率。方法采用回顾性研究的方式,收集52例首都医科大学宣武医院核医学科肺结节患者的PET/CT图像,对图像的感兴趣区域基于Contourlet变换提取灰度共生矩阵的纹理参数。对肺结节PET/CT图像的纹理参数首先采用单因素分析的方法,根据ROC曲线下面积筛选纹理参数,再对其进行主成分分析提取主要成分。基于主成分、根据ROC曲线筛选的纹理及原始纹理分别采用K最近邻分类算法建立肺结节良恶性的分类器,通过正确率、灵敏度、特异度、阳性预测值(positive predictive value,PPV)、阴性预测值(negative predictive value,NPV)、ROC曲线下面积(area under curve,AUC)这些指标评价分类效果。结果 PET/CT图像共提取1344个原始纹理参数,经单因素分析后筛选出89个纹理参数,对筛选后的纹理共提取11个主成分。基于主成分、筛选纹理、原始纹理的分类模型正确率分别为0.614、0.579、0.263;AUC分别为0.645、0.610、0.515。结论在主成分纹理、单因素分析筛选的纹理、原始纹理中,基于主成分纹理建立K最近邻分类器的效果最好。     

肺结节倍增时间的CT研究进展

目的 总结高分辨率CT测定不同性质肺结节的倍增时间对其临床随访管理决策的影响。方法 分别以“倍增时间”“生长速率”“肺结节”“早期肺癌”和“doubling time”“growth rate”“lung nodule”等为中英文关键词,在万方数据、中国知网、PubMed、Web of Science等中英文数据库中,检索1997年1月—2017年1月国内外有关倍增时间与肺结节关系的相关文献资料,并进行汇总分析。结果 不同大小、密度、病理类型的肺结节倍增时间不同,预后也不同。对于常规CT形态学和功能学检查无法判别良恶性质的肺结节,利用体积或质量倍增时间量化评估其生长速率,是提高早期肺癌诊断正确率和合理制定处理策略的手段之一;但是,肺结节倍增时间测定结果的准确度、具体方法及可重复性受多种因素影响,不同研究报道之间亦存在差异,需合理评估肺结节倍增时间的临床价值。结论 合理化随访肺结节的动态演变以及联合其倍增时间特点,可协助制定并完善临床综合管理决策,避免延误诊治或者过度治疗。     

基于PET/CT图像纹理参数的肺结节诊断模型

目的基于PET/CT融合图像纹理参数建立肺结节良恶性诊断模型,提高肺癌的识别率。方法选取宣武医院核医学科经PET/CT检查的52例肺结节患者,收集其PET/CT影像图像及人口学、影像学信息。以Contourlet变换和灰度共生矩阵相结合的方式,对PET/CT图像的感兴趣区域提取纹理参数。基于所提取的纹理参数建立支持向量机模型,得到每个肺结节良恶性判别结果。为了提高模型的诊断效果,将结节边缘、最大摄取值、有晕征等影像学信息也纳入模型,重新建立支持向量机模型。通过灵敏度、特异度、正确率等指标对模型诊断效果进行评价。结果纹理参数肺结节诊断模型的灵敏度、特异度分别为90.7%、93.5%,纹理参数结合影像学信息的肺结节诊断模型的灵敏度、特异度分别为95.7%、100.0%。结论基于PET/CT图像纹理参数建立的支持向量机模型对良恶性肺结节具有较好的鉴别诊断效果。     

原发性肺癌孤立性结节的自动提取

研究自动分割和提取原发性肺癌肺部孤立性结节(SPN)特征的方法。对CT图像进行预处理后，首先分割出肺实质，然后用模糊C均值聚类方法对肺实质图像作进一步地细分割，提取感兴趣区域(ROI)，最后根据分形理论计算出分形维数结合灰度方差供分类判决。结果表明此方法能够有效地自动识别SPN。     

基于深度学习结合解剖学注意力机制的肺结节良恶性分类

肺结节作为肺癌的初期表现,及时的发现和准确的良恶性诊断对于疾病的治疗具有重要的意义。为了提高肺部CT图像中肺结节良恶性的诊断率,提出一种基于3D ResNet的卷积神经网络,并通过加入解剖学注意力模块有效地提高了肺结节良恶性的分类精度。此外,该方法通过自动分割以获取注意力机制所需的感兴趣区域,实现整个流程的全自动化。解剖学注意力的添加能更好地捕捉图像中的局部纹理信息,进一步提取对于肺结节良恶性诊断有用的特征。本文方法在LIDC-IDRI数据集上进行验证。实验结果表明与传统的3D ResNet及其他现有的方法相比,本文方法在分类精度上有显著的提高,在独立测试集上的最终分类的AUC达到0.973,准确率为0.940。由此可见,本文方法能在辅助医生对肺结节的诊断中起到重要作用。     

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses

A new classification scheme was developed to classify mammographic masses as malignant and benign by using interval change information. The masses on both the current and the prior mammograms were automatically segmented using an active contour method. From each mass, 20 run length statistics (RLS) texture features, 3 speculation features, and 12 morphological features were extracted. Additionally, 20 difference RLS features were obtained by subtracting the prior RLS features from the corresponding current RLS features. The feature space consisted of the current RLS features, the difference RLS features, the current and prior speculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification were used to select and merge the most useful features. A leave-one-case-out resampling scheme was used to train and test the classifier using 140 temporal image pairs (85 malignant, 55 benign) obtained from 57 biopsy-proven masses (33 malignant, 24 benign) in 56 patients. An average of 10 features were selected from the 56 training subsets: 4 difference RLS features, 4 RLS features, and 1 speculation feature from the current image, and 1 speculation feature from the prior, were most often chosen. The classifier achieved an average training Az of 0.92 and a test Az of 0.88. For comparison, a classifier was trained and tested using features extracted from the 120 current single images. This classifier achieved an average training Az of 0.90 and a test Az of 0.82. The information on the prior image significantly (p = 0.015) improved the accuracy for classification of the masses.     

多层螺旋CT灌注成像技术在良恶性肺结节鉴别诊断中的应用价值

目的探讨64排螺旋CT灌注成像在孤立性肺结节的良恶性鉴别诊断中的应用价值。方法29例孤立性肺结节（直径≤4cm，20例恶性，9例良性）患者，其中男性20例，女性9例，年龄28～76岁，平均年龄59．7岁。应用64排螺旋CT对孤立性肺结节行灌注扫描（造影剂总量100m1,流速4ml／s）。将动态扫描数据输入GE工作站（ADW4．2P），应用工作站随机Perfusion 3-体部肿瘤软件包分析和测定肺内病灶的血容积（BV）、血流量（BF）、平均通过时间（MTT）、表面通透性（PS）的数值和时间-密度曲线（time—density curve，TDC），并测量孤立性肺结节增强前的CT值、强化值、结节／动脉强化值比。结果肺恶性结节的灌注参数均高于肺良性结节，BF、BV、PS值均有统计学意义（P〈0．05），BV的差异最显著。以BV≥4ml／100mg为诊断阈值，结节BV≥4ml／100mg的诊断为恶性，敏感性95％、特异性100％。恶性结节的强化值和结节／动脉强化值比均明显高于良性结节（P=0．002．P=0．020）。结论动态对比增强CT提供了孤立性肺结节的血流模式的定量信息,可用于无创性的诊断和评价孤立性肺结节。     

Classification of breast masses in ultrasonic B scans using Nakagami and K distributions

Classification of breast masses in greyscale ultrasound images is undertaken using a multiparameter approach. Five parameters reflecting the non-Rayleigh nature of the backscattered echo were used. These parameters, based mostly on the Nakagami and K distributions, were extracted from the envelope of the echoes at the site, boundary, spiculated region and shadow of the mass. They were combined to create a linear discriminant. The performance of this discriminant for the classification of breast masses was studied using a data set consisting of 70 benign and 29 malignant cases. The Az value for the discriminant was 0.96 +/- 0.02, showing great promise in the classification of masses into benign and malignant ones. The discriminant was combined with the level of suspicion values of the radiologist leading to an Az value of 0.97 +/- 0.014. The parameters used here can be calculated with minimal clinical intervention, so the method proposed here may therefore be easily implemented in an automated fashion. These results also support the recent reports suggesting that ultrasound may help as an adjunct to mammography in breast cancer diagnostics to enhance the classification of breast masses.     

18F-FDG PET/CT显像对可疑甲状腺原发病变的评估价值

目的 对18F-FDG PET/CT检查发现可疑甲状腺高代谢结节的发病率和良恶性进行回顾性分析.方法 回顾2010年11月至2013年5月间行18F-FDG PET/CT检查,发现甲状腺结节的2569例患者中,甲状腺呈高代谢灶的患者1219例,有病理证实及随访结果的67例.根据病变病理及随访结果,结合代谢情况、影像表现,对此67例甲状腺良恶性病变发病率进行分析.结果 甲状腺弥漫性病变恶性率与局灶性高代谢病变恶性率的差异有统计学意义（P＜0.01）.单发高代谢结节中,恶性结节及良性结节SUVmax值分别为6.63±4.49和3.07±1.42,两者之间差异有统计学意义（=2.436,P=0.035）.多发结节单发高代谢灶病变中,恶性及良性病变SUVmax值分别为5.72±2.47和3.37±0.74,两者之间差异无统计学意义.多发高代谢结节的良恶性SUVmax值差异无统计学意义（t=1.870,P=0.151）.结论 甲状腺高代谢结节有较高的恶性率,SUVmax值对单发高代谢结节的良恶性鉴别有意义.