Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.     

3-D segmentation algorithm of small lung nodules in spiral CT images.

Computed tomography (CT) is the most sensitive imaging technique for detecting lung nodules, and is now being evaluated as a screening tool for lung cancer in several large samples studies all over the world. In this report, we describe a semiautomatic method for 3-D segmentation of lung nodules in CT images for subsequent volume assessment. The distinguishing features of our algorithm are the following. 1) The user interaction process. It allows the introduction of the knowledge of the expert in a simple and reproducible manner. 2) The adoption of the geodesic distance in a multithreshold image representation. It allows the definition of a fusion--segregation process based on both gray-level similarity and objects shape. The algorithm was validated on low-dose CT scans of small nodule phantoms (mean diameter 5.3--11 mm) and in vivo lung nodules (mean diameter 5--9.8 mm) detected in the Italung-CT screening program for lung cancer. A further test on small lung nodules of Lung Image Database Consortium (LIDC) first data set was also performed. We observed a RMS error less than 6.6% in phantoms, and the correct outlining of the nodule contour was obtained in 82/95 lung nodules of Italung-CT and in 10/12 lung nodules of LIDC first data set. The achieved results support the use of the proposed algorithm for volume measurements of lung nodules examined with low-dose CT scanning technique.     

一种全新的基于胸片计算机辅助检测肺结节方案

针对目前基于胸片肺结节计算机辅助检测系统的检出率低,且检测结果有大量假阳性的问题,提出一种全新检测方案.该方案首先引入基于活动形状模型的算法分割肺区,在肺区中选取大量可疑结节,然后为每个可疑结节提取基于分割结果的27个特征,最后引入线性分类器对可疑结节进行分类,给出最终检测结果.方案中,由于两步结节增强技术的引入,使得只有少量真实结节在可疑结节选取过程中丢失.特征提取时,引入分水岭算法分割可疑结节,基于分割结果提取能够有效区分可疑结节中真实结节和假结节的形状特征、灰度统计特征、曲面特征和梯度特征等,并利用可疑结节分割结果与感兴趣区域中Canny算子边缘检测结果的相关性来降低假阳性.本文选择日本放射技术学会提供的公共数据库测试系统的肺结节检测性能,系统在平均每幅图4.5个假阳性水平下检测出72.2％的结节.对非常不明显和极其不明显结节,系统的检测性能在4.5个假阳性水平下达到了52.7%.     

一种基于CT影像的肺癌病灶检测新方法

 肺癌病灶的检测一直是重要与困难的工作,本文提出了一种基于三维CT影像的肺结节计算机辅助检测新方法.基于自适应阈值等方法分割肺实质区域;由于肺血管是肺结节检测的重要干扰,建立一种形变模型精确分割并过滤肺内血管组织;基于Hessian矩阵特征值构造可选择形状滤波器检测疑似结节,并进一步过滤剩余的细小血管组织;提取多个结节特征,并采用基于规则分类器进行分类.实验结果表明,该方法可以有效帮助医生提高肺癌疾病的诊断准确率.     

GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis

In lung cancer screening, benign and malignant nodules can be classified through nodule growth assessment by the registration and, then, subtraction between follow-up computed tomography scans. During the registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be aligned to that in the reference image. However, ground glass opacity (GGO) nodules are very elusive to automatically segment due to their inhomogeneous interior. In other words, it is difficult to automatically define the volume-preserving regions of GGO nodules. In this paper, we propose an accurate and fast nonrigid registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our nonrigid registration by using Compute Unified Device Architecture (CUDA). In the nonrigid registration process, the computationally expensive procedures of the floating-image transformation and the cost-function calculation are accelerated by using CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 20× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in other organs (e.g., liver cancer).     

基于语义属性的肺结节良恶性分类

现有肺结节良恶性计算机辅助诊断的依据通常为肺部CT图像的底层特征,而临床医生的诊断依据为高级语义特征.为克服这种图像底层特征和高级语义特征之间的不一致性,提出一种基于语义属性的肺结节良恶性判别方法.首先,利用阈值概率图方法提取肺结节图像;其次,一方面提取肺结节图像的形状、灰度、纹理、大小和位置等底层特征,组成样本特征集.另一方面,根据专家对肺结节属性的标注,提取结节属性集;然后,根据特征集和属性集建立属性预测模型,实现两者之间的映射;最后,利用预测的属性进行肺结节的良恶性分类.LIDC数据库上的实验结果表明所提方法具有较高的分类精度和AUC值.     

基于空间分布的三维自动化肺结节分割算法

针对肺结节分割中存在的自动化程度低、较少考虑空间结构以及粘附型肺结节分割不充分问题,提出了一种基于空间分布的三维自动化肺结节分割算法.该算法首先利用C-means聚类算法分割出肺实质,然后根据肺结节空间分布的差异性将其分为3类:孤立性肺结节、胸膜粘附性肺结节、血管粘附性肺结节,并对3种不同类型的肺结节分别采用基于连通性、灰度下降和散度差异的分割算法进行分割,70个肺结节(其中孤立性肺结节38个,血管粘附性肺结节17个,胸膜粘附性肺结节15个)CT图像的实验结果表明,算法能够准确、自动地分割出3种不同部位的肺结节.     

基于多模型融合方法的肺结节良恶性分类

针对CT图像中肺结节因边缘模糊、特征不明显造成的分类效果有偏差的问题,本文提出一种嵌入注意力机制的多模型融合方法(简称MSMA-Net).该方法先将原始CT图像进行肺实质分割和裁剪操作后得到两种不同尺寸的图像,然后分别输入到空间注意力模型和通道注意力模型进行训练,其中,空间注意力模型着重于提取肺结节在CT图像中的空间位...     

Feature subset selection for improving the performance of false positive reduction in lung nodule CAD.

We propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule computer-aided detection (CAD). It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set, and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (52 true nodules and 443 false ones) acquired by multislice CT scans. From 23 features calculated for each detected structure, the suggested method determined ten to be the optimal feature subset size, and selected the most relevant ten features. A support vector machine classifier trained with the optimal feature subset resulted in 100% sensitivity and 56.4% specificity using an independent validation set. Experiments show significant improvement achieved by a system incorporating the proposed method over a system without it. This approach can be also applied to other machine learning problems; e.g. computer-aided diagnosis of lung nodules.     

Hierarchical scale-based multiobject recognition of 3-D anatomical structures

Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.     

Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI

We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach.     

Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images

Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity–texture (JET), is based on a modified Mumford–Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to “ground truth” delineations obtained by experts and 2) copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape.      

Lung No dules Detection in CT Images Using Gestalt-Based Algorithm

To overcome low accuracy and high false positive of existing computer-aided lung nodules detec-tion. We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory. The pro-posed scheme involves two parts which simulate human eyes cognition features such as simplicity, integrity and classification. Firstly, lung region was segmented from lung Computed tomography (CT) sequences. Then local three-dimensional information was integrated into the Maximum intensity projection (MIP) images from axial, coronal and sagittal profiles. In this way, lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules. The experimental database includes fifty-three high resolution CT images contained lung nodules, which had been confirmed by biopsy. The experimental results show that, the accuracy rate of the proposed algorithm achieves 91.29%. The proposed frame-work improves performance and computation speed for computer aided nodules detection.     

Surface-bounded growth modeling applied to human mandibles

From a set of longitudinal three-dimensional scans of the same anatomical structure, we have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semilandmarks are mapped into Procrustes space. Principal component analysis extracts a one-dimensional subspace, which is used to construct a linear growth model. The worst case mean modeling error in a cross validation study is 3.7 mm.     

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automated lung nodule detection in thoracic computed tomography (CT) scans, vessel trees can be used to resolve local ambiguities based on global considerations and so improve the performance of nodule detection algorithms. In this study, a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans was developed by using correlation-based enhancement filters and a fuzzy shape representation of the data. The proposed correlation-based enhancement filters depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible. The proposed fuzzy shape representation is based on regulated morphological operations that are less sensitive to noise. Consequently, the vessel tree reconstruction algorithm can accommodate vessel bifurcation and discontinuities. A quantitative performance evaluation of the enhancement filters and of the vessel tree reconstruction algorithm was performed. Moreover, the proposed vessel tree reconstruction algorithm reduced the number of false positives generated by an existing nodule detection algorithm by 38%.     

基于CT征象量化分析的肺结节恶性度分级

为了提高肺结节恶性度分级的计算精度及可解释性,该文提出一种基于CT征象量化分析的肺结节恶性度分级方法。首先,融合影像组学特征和通过卷积神经网络提取的高阶特征构造分析CT征象所需的特征集; 接着,在混合特征集的基础上利用进化搜索机制优化集成学习分类器,实现对7种肺结节征象的识别和量化打分; 最后,将7种CT征象的量化打分输入到一个利用差分进化算法优化产生的多分类器,实现肺结节恶性度的分级计算。在实验研究中使用LIDC-IDRI数据集中的2000个肺结节样本进行进化集成学习器和恶性度分级器的训练和测试。实验结果显示对7种CT征象的识别准确率可达0.9642以上,肺结节恶性度分级的准确率为0.8618,精确率为0.8678,召回率为0.8617,F1指标为0.8627。与多个典型算法的比较显示,该文方法不但具有较高的准确率,而且可对相关CT征象进行量化分析,使得对恶性度的分级结果更具可解释性。     

Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique

The purpose of this study is to develop a technique for computer-aided diagnosis (CAD) systems to detect lung nodules in helical X-ray pulmonary computed tomography CT) images. We propose a novel template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules existing within the lung area; the GA was used to determine the target position in the observed image efficiently and to select an adequate template image from several reference patterns for quick template matching. In addition, a conventional template matching was employed to detect nodules existing on the lung wall area, lung wall template matching (LWTM), where semicircular models were used as reference patterns; the semicircular models were rotated according to the angle of the target point on the contour of the lung wall. After initial detecting candidates using the two template-matching methods, we extracted a total of 13 feature values and used them to eliminate false-positive findings. Twenty clinical cases involving a total of 557 sectional images were used in this study. 71 nodules out of 98 were correctly detected by our scheme (i.e., a detection rate of about 72%), with the number of false positives at approximately 1.1/sectional image. Our present results show that our scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.     

基于CT征象量化分析的肺结节恶性度分级

为了提高肺结节恶性度分级的计算精度及可解释性,该文提出一种基于CT征象量化分析的肺结节恶性度分级方法.首先,融合影像组学特征和通过卷积神经网络提取的高阶特征构造分析CT征象所需的特征集; 接着,在混合特征集的基础上利用进化搜索机制优化集成学习分类器,实现对7种肺结节征象的识别和量化打分; 最后,将7种CT征象的量化打分输入到一个利用差分进化算法优化产生的多分类器,实现肺结节恶性度的分级计算.在实验研究中使用LIDC-IDRI数据集中的2000个肺结节样本进行进化集成学习器和恶性度分级器的训练和测试.实验结果显示对7种CT征象的识别准确率可达0.9642以上,肺结节恶性度分级的准确率为0.8618,精确率为0.8678,召回率为0.8617,F1指标为0.8627.与多个典型算法的比较显示,该文方法不但具有较高的准确率,而且可对相关CT征象进行量化分析,使得对恶性度的分级结果更具可解释性.