CT图像肺结节自动检测

肺癌是对人类生命健康危害最大的恶性肿瘤之一。计算机辅助诊断系统对肺部CT图像进行自动分析后,可提示医生可疑肺结节,从而克服医生在诊断中的一些主观因素,为此本文提出了一种基于胸部CT图像的可疑肺结节自动检测算法。首先,根据胸部组织的特殊结构,利用一种新的分割算法提取出肺实质部分;在此基础上提取出灰度与结节相近的感兴趣区域,包括结节、肺血管、支气管;然后,以已标记的结节数据作为样本集,计算结节的面积、灰度均值、灰度方差、圆形度、形状矩、体积、球形度等特征值,利用最近邻法建立分类器判别函数;最后,计算测试集感兴趣区域的上述特征,对其进行判别、分类,并标记出结节。试验结果表明,该算法综合考虑了肺结节特征,具有较高的准确度。     

Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique.

We have been developing a computer-aided diagnostic (CAD) scheme to assist radiologists in improving the detection of pulmonary nodules in chest radiographs, because radiologists can miss as many as 30% of pulmonary nodules in routine clinical practice. A key to the successful clinical application of a CAD scheme is to ensure that there are only a small number of false positives that are incorrectly reported as nodules by the scheme. In order to significantly reduce the number of false positives in our CAD scheme, we developed, in this study, a multiple-template matching technique, in which a test candidate can be identified as a false positive and thus eliminated, if its largest cross-correlation value with non-nodule templates is larger than that with nodule templates. We describe the technique for determination of cross-correlation values for test candidates with nodule templates and non-nodule templates, the technique for creation of a large number of nodule templates and non-nodule templates, and the technique for removal of nodulelike non-nodule templates and non-nodulelike nodule templates, in order to achieve a good performance. In our study, a large number of false positives (44.3%) were removed with reduction of a very small number of true positives (2.3%) by use of the multiple-template matching technique. We believe that this technique can be used to significantly improve the performance of CAD schemes for lung nodule detection in chest radiographs.     

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.     

Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks,discriminant analysis,and a rule-based scheme

A computer-aided diagnosis (CAD) scheme is being developed to identify image regions considered suspicious for lung nodules in chest radiographs to assist radiologists in making correct diagnoses. Automated classifiers—an artificial neural network, discriminant analysis, and a rule-based scheme—are used to reduce the number of false-positive detections of the CAD scheme. The CAD scheme first detects nodule candidates from chest radiographs based on a difference image technique. Nine image features characterizing nodules are extracted automatically for each of the nodule candidates. The extracted image features are then used as input data to the classifiers for distinguishing actual nodules from the false-positive detections. The performances of the classifiers are evaluated by receiver-operating characteristic analysis. On the basis of the database of 30 normal and 30 abnormal chest images, the neural network achieves an A_Z value (area under the receiver-operating-characteristic curve) of 0.79 in detecting lung nodules, as tested by the round-robin method. The neural network, after being trained with a training database, is able to eliminate more than 83% of the false-positive detections reported by the CAD scheme. Moreover, the combination of the trained neural network and a rule-based scheme eliminates 96% of the false-positive detections of the CAD scheme.     

Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography

In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).     

基于多特征融合与XGBoost的肺结节检测

为了提高肺结节检测的精确度和效率，提出一种基于多特征融合和XGBoost的肺结节检测模型。首先采用阈值分割与形态学运算，获得候选结节区域；然后通过基于超分辨率重建的卷积神经网络进行候选结节的特征增强；其次采用快速鲁棒特征、灰度共生矩阵、灰度不变矩的提取方法获得候选结节的局部与全局的多种特征，采用词袋模型进行降维并融合；最后利用XGBoost-决策树分类模型去除假阳性结节，完成肺结节的检测。在LIDC-IDRI数据上进行的实验表明该模型能达到97.87%的准确率和97.92%的召回率。该模型可用于辅助医生进行肺结节诊断，具有一定的临床应用价值。     

Automated detection of lung nodules in CT scans: preliminary results

We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate. After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to nonnodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.     

Low-cost soft-copy display accuracy in the detection of pulmonary nodules by single-exposure dual-energy subtraction: Comparison with hard-copy viewing

This study endeavored to clarify the usefulness of single-exposure dual-energy subtraction computed radiography (CR) of the chest and the ability of soft-copy images to detect low-contrast simulated pulmonary nodules. Conventional and bone-subtracted CR images of 25 chest phantom image sets with a low-contrast nylon nodule and 25 without a nodule were interpreted by 12 observers (6 radiologists, 6 chest physicians) who rated each on a continuous confidence scale and marked the position of the nodule if one was present. Hard-copy images were 7 x 7-inch laser-printed CR films, and soft-copy images were displayed on a 21-inch noninterlaced color CRT monitor with an optimized dynamic range. Soft-copy images were adjusted to the same size as hard-copy images and were viewed under darkened illumination in the reading room. No significant differences were found between hard- and soft-copy images. In conclusion, the soft-copy images were found to be useful in detecting low-contrast simulated pulmonary nodules.     

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。     

Computerized detection of intracranial aneurysms for three-dimensional MR angiography: feature extraction of small protrusions based on a shape-based difference image technique

We have improved a computerized scheme for the detection of intracranial aneurysms for three-dimensional (3-D) magnetic resonance angiography (MRA) by the use of image features of small protrusions extracted based on a shape-based difference image (SBDI) technique. Initial candidates were identified by use of a multiple gray-level thresholding technique in dot enhanced images, and by finding short branches in skeleton images. Image features related to aneurysms were determined based on candidate regions segmented by use of a region growing technique. For extracting additional features on small protrusions or small aneurysms, we have developed an SBDI technique, which was based on the shape-based difference between an original segmented vessel and a vessel with suppressed local change in thickness. The SBDI technique was useful for obtaining local changes in vessel thickness, i.e., SBD regions, which could be small aneurysms in the case of true positives, but thin or very small regions in the case of false positives. Many false positives were removed by means of rule-based schemes and linear discriminant analysis on various 3-D localized image features, including SBDI features. We tested the computerized scheme on 53 cases with 61 aneurysms and 62 nonaneurysm cases based on a leave-one-out-by-patient test method. As a result, false positives per patient decreased from 5.8 to 3.8, while a high sensitivity of 97% was maintained by use of the SBDI technique, in which SBDI features were effective for removing some false positives. The computer-aided diagnostic (CAD) scheme may be robust and useful in assisting radiologists in the detection of intracranial aneurysms for MRA.     

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.     

基于收敛指数滤波和Hessian矩阵的肺结节检测算法

肺癌的早期形态多以肺结节的形式出现,对其正确检测有助于提高肺癌病人的存活率.针对肺部高分辨率CT图像中肺结节与血管横断面难以区分的问题,提出了一种基于收敛指数滤波和Hessian矩阵的肺结节检测算法.首先对基于向量域的收敛指数滤波器进行量化产生候选肺结节,然后设计基于三阶Hessian矩阵特征值的血管检测滤波器对血管进行检测标记.最后从候选肺结节中剔除血管横断面得到真阳性肺结节.实验结果 表明,本文提出的检测算法具有较高的灵敏性和低假阳性.     

基于3D ResUnet网络的肺结节分割

目的:将深度残差结构和U-Net网络结合形成新的网络ResUnet,并利用ResUnet深度学习网络结构对胸部CT影像进行图像分割以提取肺结节区域。方法:使用的CT影像数据来源于LUNA16数据集,首先对CT图像预处理提取出肺实质,然后对其截取立体图像块并进行数据增强来扩充样本数,形成相应的肺结节掩膜图像,最后将生成的样本输入到ResUnet模型中进行训练。结果:本研究模型最终的精度和召回率分别为35.02%和97.68%。结论:该模型能自动学习肺结节特征,为后续的肺癌自动诊断提供可靠基础,减少临床诊断的成本并节省医生诊断的时间。 【关键词】肺结节;分割;深度残差结构;召回率;ResUnet     

Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key "spiral-scanning" technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the "north pole" to the "south pole." The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the "optimal" outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.     

非对称卷积核YOLO V2网络的CT影像肺结节检测

肺癌一直是严重威胁人类健康的疾病之一,肺结节作为早期肺癌的一个重要征象,在肺癌的早期诊断与治疗中具有重要的意义。传统的CT影像肺结节检测方法不仅步骤繁琐、处理速度慢,而且对于结节的检出率及定位精度都亟待提高。提出一种基于非对称卷积核YOLO V2网络的CT影像肺结节检测方法:首先将连续的CT序列叠加构造为伪彩色数据集,以增强病变和健康组织的差异;然后将含有非对称卷积核的inception V3模块引入到YOLO V2网络中,构造出一种适用于肺结节检测的深度网络,一方面利用YOLO V2网络在目标检测上的优势,另一方面通过inception V3模块在网络的宽度与深度上进行扩增,以提取更加丰富的特征;为进一步提高结节的定位精度,对损失函数的设计与计算方法也进行一定的改进。为验证所提检测模型的性能,从LIDC-IDRI数据集中选取1 010个病例的CT图像用于训练和测试,在大于3 mm的肺结节中,检测敏感度为94.25%,假阳性率为8.50%。实验表明,所提出的肺结节检测方法不仅可以简化肺部CT图像的处理过程,而且在结节检测率及定位精度方面均优于传统方法,可为肺结节检测提供一种新思路。     

Effect of Multiscale Processing in Digital Chest Radiography on Automated Detection of Lung Nodule with a Computer Assistance System

The aim of this study is to evaluate the effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer-aided diagnosis (CAD) system. The study involved 58 small-nodule patient cases and 58 normal cases. The 58 patient cases included a total of 64 noncalcified lung nodules up to 15 mm in diameter. Each case underwent an examination with a digital radiography system (Digital Diagnost, Philips Medical Systems), and the acquired image was processed by the following three types of multiscale processing (Unique Image Processing Package, Philips Medical Systems) respectively: (1) standard image from the default processing parameter (structure preference, 0.0), (2) high-pass image with structure preference of 0.4, (3) low-pass image with structure preference of ?0.4. The CAD output images were produced with a real-time computer assistance system (IQQA?-Chest, EDDA Technology). Two experienced chest radiologists established the nodule gold standard by consensus reading according to computed tomography results, and analyzed and recorded the detection of lung nodules and false-positive detections of these CAD output images. For the entire cases involved (each case with three types of different processing), a total of 348 observations were evaluated by the receiver operating characteristic (ROC) analysis. The mean area under the ROC curve (A _z ) value was 0.700 for the standard images, 0.587 for the high-pass images, and 0.783 for the low-pass images. There were statistically significant A _z values among these three types of processed images (p?<?0.01). Multiscale processing in digital chest radiography can affect the automated detection of lung nodule by CAD, which is consistent with effects from visual inspection.     

Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers.

We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (Az). The classifier was optimized by adding additional features until the Az declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an Az of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1 %. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives.     

Preliminary Evaluation of Biplane Correlation (BCI) Stereographic Imaging for Lung Nodule Detection

A biplane correlation (BCI) imaging system obtains images that can be viewed in stereo, thereby minimizing overlapping structures. This study investigated whether using stereoscopic visualization provides superior lung nodule detection compared to standard postero-anterior (PA) image display. Images were acquired at two oblique views of ±3° as well as at a standard PA position from 60 patients. Images were processed using optimal parameters and displayed on a stereoscopic display. The PA image was viewed in the standard format, while the oblique views were paired to provide a stereoscopic view of the subject. A preliminary observer study was performed with four radiologists who viewed and scored the PA image then viewed and scored the BCI stereoscopic image. The BCI stereoscopic viewing of lung nodules resulted in 71 % sensitivity and 0.31 positive predictive value (PPV) index compared to PA results of 86 % sensitivity and 0.26 PPV index. The sensitivity for lung nodule detection with the BCI stereoscopic system was reduced by 15 %; however, the total number of false positives reported was reduced by 35 % resulting in an improved PPV index of 20 %. The preliminary results indicate observer dependency in terms of relative advantage of either system in the detection of lung nodules, but overall equivalency of the two methods with promising potential for BCI as an adjunct diagnostic technique.     

Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index

We present a number of approaches based on the radial gradient index (RGI) to achieve false-positive reduction in automated CT lung nodule detection. A database of 38 cases was used that contained a total of 82 lung nodules. For each CT section, a complementary image known as an "RGI map" was constructed to enhance regions of high circularity and thus improve the contrast between nodules and normal anatomy. Thresholds on three RGI parameters were varied to construct RGI filters that sensitively eliminated false-positive structures. In a consistency approach, RGI filtering eliminated 36% of the false-positive structures detected by the automated method without the loss of any true positives. Use of an RGI filter prior to a linear discriminant classifier yielded notable improvements in performance, with the false-positive rate at a sensitivity of 70% being reduced from 0.5 to 0.28 per section. Finally, the performance of the linear discriminant classifier was evaluated with RGI-based features. RGI-based features achieved a substantial improvement in overall performance, with a 94.8% reduction in the false-positive rate at a fixed sensitivity of 70%. These results demonstrate the potential role of RGI analysis in an automated lung nodule detection method.     

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.