基于局部灰度最大和改进Mahalanobis

CT图像中肺结节检测一直是肺癌CAD系统的关键和难点。提出了一种孤立性肺结节自动检测算法,首先对原始CT图像进行有效、准确的肺实质分割;采用寻找局部灰度最大值方法对ROI进行初始分割;再对分割出的各ROI进行特征提取,利用SVM方法对每个特征进行定量描述,根据SVM单特征分类准确率对Mahalanobis距离进行加权改进,最后采用基于改进的Mahalanobis距离进行肺结节分类。实验结果表明,该算法可以较好地提取出CT图像中的孤立性肺结节,具有较高的灵敏度和较低的漏诊率,可以为医生诊断早期肺癌病灶提供帮助信息。     

基于局部灰度最大和改进Mahalanobis距离分类的肺结节检测算法

CT图像中肺结节检测一直是肺癌CAD系统的关键和难点。提出了一种孤立性肺结节自动检测算法,首先对原始CT图像进行有效、准确的肺实质分割;采用寻找局部灰度最大值方法对ROI进行初始分割;再对分割出的各ROI进行特征提取,利用SVM方法对每个特征进行定量描述,根据SVM单特征分类准确率对Mahalanobis距离进行加权改进,最后采用基于改进的Mahalanobis距离进行肺结节分类。实验结果表明,该算法可以较好地提取出CT图像中的孤立性肺结节,具有较高的灵敏度和较低的漏诊率,可以为医生诊断早期肺癌病灶提供帮助信息。     

CT图像肺结节的毛刺检测与量化评估

为准确检测并量化评估毛刺征,提出一种CT图像肺结节的毛刺检测与量化评估方法。首先利用区域生长算法与水平集方法结合进行结节主体的准确分割;而后利用线性滤波模板提取结节主体周边区域的毛刺;最后引入毛刺水平指数作为毛刺特征的量化指标。在此基础上对结节有无毛刺进行分类,并与肺部图像数据库联盟(LIDC)的量化评级进行一致性和相关性分析。实验结果表明,该方法可以有效地检测并定量描述CT图像肺结节的毛刺征。     

Sensitivity improvement of automatic pulmonary nodules detection in chest X-ray CT images

In this article, we develop an automatic detection method for non-isolated pulmonary nodules as part of a computer-aided diagnosis (CAD) system for lung cancers in chest X-ray computed tomography (CT) images. An essential core of the method is to separate non-isolated nodules from connecting structures such as the chest wall and blood vessels. The isolated nodules can be detected more easily by the CAD systems developed previously. To this end, we propose a preprocessing technique for nodule candidate detection by using double-threshold binarization. We evaluate the performance using the receiver operating characteristic (ROC) analysis in clinical chest CT images. The results suggest that the detection rate for non-isolated nodules by the proposed method is superior to that by the conventional preprocessing methods.     

A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

BackgroundDetection and monitoring of respiratory related illness is an important aspect in pulmonary medicine. Acoustic signals extracted from the human body are considered in detection of respiratory pathology accurately.ObjectivesThe aim of this study is to develop a prototype telemedicine tool to detect respiratory pathology using computerized respiratory sound analysis.MethodsAround 120 subjects (40 normal, 40 continuous lung sounds (20 wheeze and 20 rhonchi)) and 40 discontinuous lung sounds (20 fine crackles and 20 coarse crackles) were included in this study. The respiratory sounds were segmented into respiratory cycles using fuzzy inference system and then S-transform was applied to these respiratory cycles. From the S-transform matrix, statistical features were extracted. The extracted features were statistically significant with p < 0.05. To classify the respiratory pathology KNN, SVM and ELM classifiers were implemented using the statistical features obtained from of the data.ResultsThe validation showed that the classification rate for training for ELM classifier with RBF kernel was high compared to the SVM and KNN classifiers. The time taken for training the classifier was also less in ELM compared to SVM and KNN classifiers. The overall mean classification rate for ELM classifier was 98.52%.ConclusionThe telemedicine software tool was developed using the ELM classifier. The telemedicine tool has performed extraordinary well in detecting the respiratory pathology and it is well validated.     

Hybrid signal processing and machine intelligence techniques for detection,quantification and classification of power quality disturbances

This paper presents an advanced signal processing technique known as S-transform (ST) to detect and quantify various power quality (PQ) disturbances. ST is also utilized to extract some useful features of the disturbance signal. The excellent time–frequency resolution characteristic of the ST makes it an attractive candidate for analysis of power system disturbance signals. The number of features required in the proposed approach is less than that of the wavelet transform (WT) for identification of PQ disturbances. The features extracted by using ST are used to train a support vector machine (SVM) classifier for automatic classification of the PQ disturbances. Since the proposed methodology can reduce the features of disturbance signal to a great extent without losing its original property, it efficiently utilizes the memory space and computation time of the processor. Eleven types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of ST and SVM can effectively detect and classify different PQ disturbances.     

深度卷积神经网络胸片肺结节分类识别研究

针对肺结节特征复杂且不明显,难以精确诊断出胸片中是否含有肺结节的问题,提出将深度神经网络应用于肺结节分类识别之中。首先通过将胸片灰度一致化,减少由于不同设备导致胸片亮度与灰度的差异;其次采用不同的数据扩增方法使得深度卷积神经网络可以充分提取肺结节的特征;最后通过改进的神经网络架构对肺结节进行分类识别。提出的算法有效地避免了在对胸片图像进行分割时造成图像特征部分丢失的现象,同时克服了由于胸片图像的复杂造成的肺结节特征不明显的缺点。最终通过实验研究证明胸片肺结节分类识别的平均准确率达到84.2%,在医学胸片肺结节的分类识别领域上具有一定的应用价值。     

Pulmonary nodule diagnosis using dual‐modal supervised autoencoder based on extreme learning machine

In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagnosis method using dual‐modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data. The network is fed with nodule images in pairs obtained from computed tomography and positron emission tomography respectively. For each pair image, the high‐level discriminative features of nodules in computed tomography and positron emission tomography are extracted from stacked supervised autoencoder layers. The outputs of the proposed architecture are combined using an ideal fusion method to get the final classification. In the experiments, 5‐fold cross‐validation method is used to validate the proposed method on 1,600 pulmonary nodule images and our method reaches high‐classification sensitivities of 91.75% at 1.58 false positives per scan. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.     

肺结节球表面网格向量化特征分类

目的 基于球谐函数与容斥映射算法向量化球面表面纹理与结节形状用以进行胸部CT图像肺结节良恶性判定。区别于基于深度学习解决肺结节良恶性筛查的方法,目前方法多集中于框架改进而忽略了数据预处理,文中所提方法旨在对球面纹理与结节形状进行向量表达,使其可以输入深度森林进行特征分类训练。方法 首先采用辽宁中医药大学附属医院数据,通过3维重构获得3维肺结节图像。其次使用球谐函数与容斥映射算法在保留空间信息的同时将纹理以网格方式映射到标准球面上。再次使用网格-LBP与映射形变能量分别完成对球面纹理与结节形状信息的构建。最后提出一种基于网格的多粒度扫描方法对深度森林训练框架进行改进,并将向量化后的纹理和形状特征加入到改进的深度森林训练框架中进行实验验证。结果 通过大量的实验结果验证,在准确率（ACC）、特异度（SPE）、敏感度（SEN）和受试者工作特征曲线下的面积（AUC）4个衡量指标下,本文方法具有优于现存先进方法的表现,其中ACC、SPE、SEN和AUC分别达到76.06%、69.46%、88.46%和0.84。结论 基于球谐函数与容斥映射算法可成功地对肺结节表面和形状两个特征进行向量化并训练,不仅考虑了数据预处理,而且通过两个特征对肺结节良恶性检测的准确率要高于传统1个特征检测的结果,同时也为3维模型中特征的提取及向量化提供了一个有效的方法。     

Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks

Lung nodule classification is one of the main topics related to computer-aided detection systems. Although convolutional neural networks (CNNs) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in chest X-ray (CXR) images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic curve of 7.76 considering 10 false positives per image (FPPI), and sensitivity values of 73.1% and 79.6% with 2 and 5 FPPI, respectively.     

融合深度网络和浅层纹理特征的甲状腺结节癌变超声图像诊断

目的 在甲状腺结节图像中对甲状腺结节进行良恶性分析,对于甲状腺癌的早期诊断有着重要的意义。随着医疗影像学的发展,大部分的早期甲状腺结节可以在超声图像中准确地检测出来,但对于结节的性质仍然缺乏准确的判断。因此,为实现更为准确的早期甲状腺结节良恶性超声图像诊断,避免不必要的针刺或其他病理活检手术、减轻病患生理痛苦和心理压力及其医疗费用,提出一种基于深度网络和浅层纹理特征融合的甲状腺结节良恶性分类新算法。方法 本文提出的甲状腺结节分类算法由4步组成。首先对超声图像进行尺度配准、人工标记以及图像复原去除以增强图像质量。然后,对增强的图像进行数据扩展,并作为训练集对预训练过的GoogLeNet卷积神经网络进行迁移学习以提取图像中的深度特征。同时,提取图像的旋转不变性局部二值模式（LBP）特征作为图像的纹理特征。最后,将深度特征与图像的纹理特征相融合并输入至代价敏感随机森林分类器中对图像进行良恶性分类。结果 本文方法在标准的甲状腺结节癌变数据集上对甲状腺结节图像取得了正确率99.15%,敏感性99.73%,特异性95.85%以及ROC曲线下面积0.997 0的的好成绩,优于现有的甲状腺结节图像分类方法。结论 实验结果表明,图像的深度特征可以描述医疗超声图像中病灶的整体感官特征,而浅层次纹理特征则可以描述超声图像的边缘、灰度分布等特征,将二者统一的融合特征则可以更为全面地描述图像中病灶区域与非病灶区域之间的差异以及不同病灶性质之间的差异。因此,本文方法可以准确地对甲状腺结节进行分类从而避免不必要手术、减轻病患痛苦和压力。     

应用规则的肺结节识别系统

为了检测胸部CT图像中的肺结节,提出一种基于应用规则的自动识别肺结节的系统。在识别系统中通过自动阈值法和轮廓跟踪法分割肺实质;采用OTSU算法分割肺实质中的感兴趣区域,对感兴趣区域的特征进行提取;选择对肺结节和血管区别度较大的特征。根据选取的这些特征设定识别肺结节的规则来确定肺结节的候选区域。实验结果表明,该系统对直径1 cm以上的结节具有较好的识别性能。     

融合注意力机制和特征金字塔网络的CT图像肺结节检测

目的 针对现有肺结节检测算法存在的因肺部计算机断层扫描（computed tomography,CT）图像肺结节与周边组织复杂性导致结节本身结构差异性不明显的问题,以及特征提取网络多次下采样造成图像分辨率降低进而导致检测结果差、仅使用网络顶层特征图进行预测造成图像空间信息丢失进而导致小结节漏检等问题,提出了一种基于注意力机制和特征金字塔的肺结节检测算法。方法 根据语义与空间特征补偿机制以及卷积神经网络中网络深度所提取特征的信息量不同,在以ResNet为骨干网络的特征提取网络中设计通道—空间注意力机制,尽可能同时获取含有较多上下文语义以及空间位置信息的特征信息。在网络预测部分设计特征金字塔网络,将高维带有丰富语义信息的特征图与低维带有位置信息的特征图融合进行多尺度预测,增强网络对于小结节以及近血管结节等非显著性目标的检测性能。结果 在LUNA16（lung nodule analysis 16）数据集上进行十折交叉验证显示,当平均假阳性个数为25.99时敏感度达到了97.13%,与基准方法相比,敏感度提高了2.53%,平均假阳性降低了28.54,实现了高敏感度低假阳性;在0.125、0.25、0.5、1、2、4、8这7个假阳率点的敏感度平均值为0.854,其中在每个扫描4次和8次假阳性时敏感度分别达到了0.940和0.951,其效果优于主流的结节检测方法。结论 提出的结节检测模型,可以提高对3~10 mm小结节、近血管结节等非显著性目标的检测性能,并具有较低的假阳率。     

基于改进LVQ算法的肺结节识别与分类

目前,肺癌的是发病率最高的肿瘤,若能在早期发现癌变并进行相应治疗,将极大的提高患者的生存率。肺癌的症状在早期表现为肺结节。以提高肺结节检测识别率并进行良恶性分类为目的,提出了一种改进的LVQ分类器算法。首先使用C-V算法对原始图像进行肺实质分割,再使用最优阈值法进行感兴趣区域提取,并进行特征提取和特征归一化。使用多次聚类算法检测肺结节。使用基于改进的LVQ分类器进行肺结节的良恶性进行分类。利用改进后的LVQ分类器在LIDC数据集上进行实验,得到了对良性结节的确诊率为87.3%,对恶性结节的确诊率为80.8%。实验结果表明,改进后的算法在良恶性结节分类上具有较高的确诊率,有助于提高医生的工作效率,实现肺结节的辅助发现。     

基于局部闭值和聚类中心迭代的肺结节检测算法

肺部疾病通常以肺结节的形式表现出来。为了对肺部疾病进行诊断治疗,需要对肺结节进行准确的检测。提出了基于局部阈值和聚类中心迭代的肺结节检测算法。首先,对肺实质图像采用局部阈值算法,提取感兴趣区域(ROIs),并且计算ROIs的形态特征、灰度特征和纹理特征;其次,结合规则、聚类中心迭代和欧式距离,对ROIs进行分类。实验结果表明,所提算法能够较好地检测出孤立性结节、低对比度结节和粘连肺壁结节。     

应用于CT图像肺结节检测的深度学习方法综述

肺癌是世界上死亡率最高的癌症，通过胸部CT影像检测肺结节对肺癌早期诊断和治疗意义重大。为了减轻放射科医生的工作量以及同时减少误诊率和漏诊率，研究人员提出了计算机辅助检测（CAD）系统辅助放射科医生检测和诊断肺结节。目前，研究人员正在尝试不同的深度学习技术，以提高计算机辅助诊断系统在基于CT图像的肺癌筛查中的性能。这项工作回顾了作为肺癌检测的CAD系统目前典型的深度学习的算法和框架，主要从数据集介绍、2D深度学习方法、3D深度学习方法、数据不平衡问题的处理、模型训练方法以及模型可解释性这六个方面进行介绍。最后，对各个方法的主要特点和算法性能进行了综合比较分析，并对如何提高结节检测性能进行了展望。     

基于双模态深度自编码的孤立性肺结节诊断方法

近年来,深度学习技术在肺癌诊断方面得到了广泛的应用,但现有的研究主要集中于肺部CT图像。为了有效提高肺结节的诊断性能,提出一种基于双模态深度降噪自编码的肺结节诊断方法。首先,分别从肺部CT和PET图像中得到肺结节区域的特征信息;然后,以候选结节的PET/CT图像作为整个深度自编码网络的输入,并对高层信息进行学习;最后,采用融合策略对多种特征进行融合并将其作为整个框架的输出。实验结果表明,提出的方法可以达到92.81%的准确率、91.75%的敏感度和1.58%的特异性,且优于其他方法的诊断性能,更适用于肺结节良/恶性的辅助诊断。