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.     

An ROC Study of Chest Radiographs: 2K Versus 4K High-Resolution Soft-Copy Images

Computed radiography of chest with a 4K image array was recently introduced. We performed a multiobserver study to compare the diagnostic accuracy of 2K (standard) and 4K (high quality) chest radiographs displayed on a 5-mega-pixel monitor (2K monitor). One hundred cases of posteroanterior chest radiographs (a total of 200 images) were selected by two chest radiologists. Those radiographs included pneumothorax (n = 14), nodules (n = 15), interstitial disease (n = 10), or neither abnormality (n = 61). These were interpreted by four radiologists in two separate sessions. They recorded their confidence scale for the presence or absence of abnormality. Diagnostic accuracy was determined by receiver operating characteristic (ROC) analysis for each observer. ROC analysis showed no statistically significant difference between the 2K and 4K modes for the detection of any of the different abnormalities by individual readers. Our preliminary study suggests that 2K mode would be sufficient for the detection of abnormality on chest radiograph and there is no considerable validity to incline toward the 4K mode in current picture archiving and communication system environment using 2K monitor. However, we think that additional investigation using more subtle parenchymal or rib lesion should be followed.     

A comparison of conventional screen-film radiography and hard copy of computed radiography in full and two-thirds sizes in detection of interstitial lung disease

This study examined whether hard-copy radiographs produced from computed radiography (CR) images show the subtle interstitial pulmonary disease equally well to conventional screen-film radiographs, because a digital radiography should be chosen for introduction of the digital picture archiving and communication system (PACS) for the new Osaka University Hospital.^1,2 Eleven radiologists examined 20 abnormal and 20 control chest radiographs presented in each of three groups: conventional screen-film radiographs and two sizes of hard-copy radiographs made from CR images. This study of digital image quality of chest examinations found that some findings on conventional screen-film radiography images are not reproduced by current CR (2,000×2,000×10 bits in matrix), especially when the experienced radiologists were observed. This finding suggested improvements are needed in CR before CR of chest should fully replace conventional screen-film radiography.     

Computerized Analysis of Pneumoconiosis in Digital Chest Radiography: Effect of Artificial Neural Network Trained with Power Spectra

It is difficult for radiologists to classify pneumoconiosis with small nodules on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on the rule-based plus artificial neural network (ANN) method for distinction between normal and abnormal regions of interest (ROIs) selected from chest radiographs with and without pneumoconiosis. The image database consists of 11 normal and 12 abnormal chest radiographs. These abnormal cases included five silicoses, four asbestoses, and three other pneumoconioses. ROIs (matrix size, 32 × 32) were selected from normal and abnormal lungs. We obtained power spectra (PS) by Fourier transform for the frequency analysis. A rule-based method using PS values at 0.179 and 0.357 cycles per millimeter, corresponding to the spatial frequencies of nodular patterns, were employed for identification of obviously normal or obviously abnormal ROIs. Then, ANN was applied for classification of the remaining normal and abnormal ROIs, which were not classified as obviously abnormal or normal by the rule-based method. The classification performance was evaluated by the area under the receiver operating characteristic curve (Az value). The Az value was 0.972 ± 0.012 for the rule-based plus ANN method, which was larger than that of 0.961 ± 0.016 for the ANN method alone (P ≤ 0.15) and that of 0.873 for the rule-based method alone. We have developed a rule-based plus pattern recognition technique based on the ANN for classification of pneumoconiosis on chest radiography. Our CAD system based on PS would be useful to assist radiologists in the classification of pneumoconiosis.     

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.     

Digital tomosynthesis of the chest for lung nodule detection: interim sensitivity results from an ongoing NIH-sponsored trial

The authors report interim clinical results from an ongoing NIH-sponsored trial to evaluate digital chest tomosynthesis for improving detectability of small lung nodules. Twenty-one patients undergoing computed tomography (CT) to follow up lung nodules were consented and enrolled to receive an additional digital PA chest radiograph and digital tomosynthesis exam. Tomosynthesis was performed with a commercial CsI/a-Si flat-panel detector and a custom-built tube mover. Seventy-one images were acquired in 11 s, reconstructed with the matrix inversion tomosynthesis algorithm at 5-mm plane spacing, and then averaged (seven planes) to reduce noise and low-contrast artifacts. Total exposure for tomosynthesis imaging was equivalent to that of 11 digital PA radiographs (comparable to a typical screen-film lateral radiograph or two digital lateral radiographs). CT scans (1.25-mm section thickness) were reviewed to confirm presence and location of nodules. Three chest radiologists independently reviewed tomosynthesis images and PA chest radiographs to confirm visualization of nodules identified by CT. Nodules were scored as: definitely visible, uncertain, or not visible. 175 nodules (diameter range 3.5-25.5 mm) were seen by CT and grouped according to size: < 5, 5-10, and > 10 mm. When considering as true positives only nodules that were scored definitely visible, sensitivities for all nodules by tomosynthesis and PA radiography were 70% (+/- 5%) and 22% (+/- 4%), respectively, (p < 0.0001). Digital tomosynthesis showed significantly improved sensitivity of detection of known small lung nodules in all three size groups, when compared to PA chest radiography.     

Interpretation of subtle interstitial chest abnormalities: Conventional radiography versus high-resolution storage-phosphor radiography—A preliminary study

To evaluate the reliability of digital chest radiography in diagnosing subtle interstitial lung abnormalities, we performed several clinical studies including a comparison of conventional screen-film radiography and storage-phosphor radiography (2 K × 2 K pixels, 10 bit), and a comparison of conventional screen-film radiography and film-digitized radiography (2 K × 2 K pixels, 10 bit). From these previous studies, a spatial resolution of 0.2-mm pixel size was considered inadequate to diagnose subtle interstitial lung diseases. Under these circumstances, the newly developed Fuji Computed Radiography system (FCR 9000; Fuji Photo Film, Tokyo, Japan) has recently become available. This system provides 0.1-mm pixel size (4 K × 5 K pixels, 10-bit depth) and life-size hard copies (14×17 inches). To evaluate the reliability of new high-resolution storagephosphor radiography (FCR 9000) in diagnosing simulated subtle interstitial abnormalities (including simulated lines, micronodules, and groundglass opacities), the differences among radiologists in interpreting conventional screen-film radiographs and life-size high-resolution storage-phosphor radiographs were studied. Observation was made by eight experienced chest radiologists, and receiver-operating characteristic (ROC) analysis was performed. There was no significant difference in detecting in subtle simulated interstitial abnormalities between conventional film-screen radiography and high-resolution storage-phosphor radiography. For all three types of abnormalities, there was no significant difference between conventional and storage-phosphor radiography. In conclusion, the high-resolution storage-phosphor chest radiography (0.1-mm pixel size, 10-bit depth) may be substituted for conventional chest radiography in the detection of subtle interstitial abnormalities.     

Detection of lung nodules in digital chest radiographs using artificial neural networks: A pilot study

Radiologists can fail to detect up to 30% of pulmonary nodules in chest radiographs. A back-propagation neural network was used to detect lung nodules in digital chest radiographs to assist radiologists in the diagnosis of lung cancer. Regions of interest (ROIs) that cantained nodules and normal tissues in the lung were selected from digitized chest radiographs by a previously developed computer-aided diagnosis (CAD) scheme. Different preprocessing techniques were used to produce input data to the neural network. The performance of the neural network was evaluated by receiver operating characteristic (ROC) analysis. We found that subsampling of original 64- × 64-pixel ROIs to smaller 8- × 8-pixel ROIs provides the optimal preprocessing for the neural network to distinguish ROIs containing nodules from false-positive ROIs containing normal regions. The neural network was able to detect obvious nodules very well with an A_z value (area under ROC curve) of 0.93, but was unable to detect subtle nodules. However, with a training method that uses different orientations of the original ROIs, we were able to improve the performance of the neural network to detect subtle nodules. Artificial neural networks have the potential to serve as a useful classifier to help to eliminate the false-positive detections of the CAD scheme.     

隐蔽的肺部小结节病灶的X线诊断传统与数字化摄影的ROC评价

目的 :对比研究数字化X线摄影和传统X线摄影对发现与正常解剖结构重叠的肺部小结节病灶的诊断价值。方法 :选取 3 0例经手术病理证实的肺部结节病例和 3 0例经CT证实无肺部结节的病例 ,分别摄取传统胸片 (A组 )和数字化胸片 (B组 ) ,由 4位高年资医生和 4位低年资医生分别对以上 60对胸片进行观察 ,结果采用ROC曲线统计法进行统计。结果 :对高年资医生来说 ,数字化胸片 (B组 )得到的ROC曲线下面积 (Az =0 .83 7)大于传统胸片 (Az =0 .82 3 ) ,两者有统计学显著性差异 (P <0 .0 5 ) ;对低年资医生来说 ,数字化胸片 (B组 )得到的ROC曲线下面积 (Az =0 .842 )大于传统胸片 (Az =0 .717) ,两者有统计学显著性差异 (P <0 .0 5 ) ;对所有医生来说 ,数字化胸片 (B组 )得到的ROC曲线下面积 (Az =0 .840 )大于传统胸片 (Az =0 .770 ) ,两者有统计学显著性差异 (P〈0 .0 5 )。结论 :数字化胸片对于发现与正常解剖结构重叠的肺内单发结节病灶优于传统胸片。     

Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification

We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.     

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.     

Quantitative Kinetic Analysis of Lung Nodules Using the Temporal Subtraction Technique in Dynamic Chest Radiographies Performed with a Flat Panel Detector

Early detection and treatment of lung cancer is one of the most effective means of reducing cancer mortality, and to this end, chest X-ray radiography has been widely used as a screening method. A related technique based on the development of computer analysis and a flat panel detector (FPD) has enabled the functional evaluation of respiratory kinetics in the chest and is expected to be introduced into clinical practice in the near future. In this study, we developed a computer analysis algorithm to detect lung nodules and to evaluate quantitative kinetics. Breathing chest radiographs obtained by modified FPD and breath synchronization utilizing diaphragmatic analysis of vector movement were converted into four static images by sequential temporal subtraction processing, morphological enhancement processing, kinetic visualization processing, and lung region detection processing. An artificial neural network analyzed these density patterns to detect the true nodules and draw their kinetic tracks. Both the algorithm performance and the evaluation of clinical effectiveness of seven normal patients and simulated nodules showed sufficient detecting capability and kinetic imaging function without significant differences. Our technique can quantitatively evaluate the kinetic range of nodules and is effective in detecting a nodule on a breathing chest radiograph. Moreover, the application of this technique is expected to extend computer-aided diagnosis systems and facilitate the development of an automatic planning system for radiation therapy.     

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.     

Detection of interstitial lung abnormalities on picture archive and communication system video monitors

The purpose of this study was to compare the detection of interstitial lung abnormalities on video display workstation monitors between radiologists experienced with video image interpretation and radiologists who lack this experience. Twenty-four patients with interstitial lung abnormalities documented by high-resolution computed tomography (HRCT) and lung biopsy, and 26 control patients with no history of pulmonary disease or a normal HRCT and normal chest radiographs were studied. Images were acquired using storage phosphor digital radiography and displayed on 1,640×2,048 pixel resolution video monitors. Five board-certified radiologists evaluated the images in a blinded and randomized manner by using a six-point presence of abnormality grading scale. Three radiologists were from 1 to 4 years out of residency and considered to be experienced workstation monitor readers with between 1 to 3 years of video monitor image interpretation. For the inexperienced readers, one radiologist had no prior experience with reading images from a video monitor and was direct out of residency, and the other radiologist had less than 4 months of intermittent exposure and was 1 year out of residency. Sensitivity and specificity were determined for individual readers. Positive predictive values, negative predictive values, accuracy, and receiver-operating curves were alsoggenerated. A comparison was made between experienced and inexperienced readers. For readers experienced with video monitor image interpretation, the sensitivity ranged from 87.5% to 92%, specificity from 69% to 92%, positive predictive value (PPV) from 73% to 87.5%, negative predictive value (NPV) from 87% to 90%, and accuracy from 80% to 88%. For inexperienced readers, these values were sensitivity 58%, specificity 50% to 65% PPV 52% to 61%, NPV 56.5% to 63%, and accuracy 54% to 62%. Comparing image interpretation between experienced and inexperienced readers, there were statistically significant differences for sensitivity (P<.01), specificity (P<.01), PPV (P<.05), NPV (P<.05), accuracy (P<.05), and area under the receiver operator curve (Az) (P<.01). Within the respective experienced and inexperienced groups, no statistical significant differences were present. Our results show that digitally acquired chest radiographs displayed on high-resolution workstation monitors are adequate for the detection of interstitial lung abnormalities when the images are interpreted by radiologists experienced with video image interpretation. Radiologists inexperienced with video monitor image interpretation, however, cannot reliably interpret images for the detection of interstitial lung abnormalities.     

肺移植后患者传统屏-片与计算机数字化系统床边胸片的质量对比

背景：肺移植后患者床边胸片的质量关系到对肺部病变的评价,对临床具有十分重要的价值。 目的：比较肺移植后患者应用传统屏-片组合和计算机数字化系统进行床边胸部摄片的图像质量,以选择优良方案。 方法：回顾性分析南京医科大学附属无锡市人民医院78例肺移植后患者床边胸片传统屏-片摄影425张和计算机数字化摄影411张的图像资料,提出优质片评估标准,经3位高年资医师、技师读片将其从优质片到废片分为Ⅰ~Ⅳ级,然后分析影响两组床边胸片质量的因素,并计算两组的平均曝光剂量。 结果与结论：肺移植后患者床边胸片,传统屏-片组：Ⅰ级片135张(31.8%)、Ⅱ级片171张(40.2%)、Ⅲ级片107张(25.2%)、Ⅳ级片12张(2.8%);计算机数字化摄影组：Ⅰ级片266张(64.7%)、Ⅱ级片105张(25.5%)、Ⅲ级片37张(9.0%)、Ⅳ级片3张(0.7%),两组床边胸片图像质量分级差异有非常显著性意义(P  < 0.01)。计算机数字化摄影组平均曝光剂量1.56 mA•s明显小于屏-片组3.27 mA•s(P < 0.01)。提示肺移植后患者计算机数字化摄影系统床边胸片质量明显优于传统屏-片组合床边胸片,应用计算机数字化摄影系统可提高优质片,减少废片,降低X射线照射剂量,可作为肺移植后患者床边胸片的首选。     

Impact of PACS on Dictation Turnaround Time and Productivity

This study was conducted to measure the impact of PACS on dictation turnaround time and productivity. The radiology information system (RIS) database was interrogated to calculate the time interval between image production and dictation for every exam performed during three 90-day periods (the 3 months preceding PACS implementation, the 3 months immediately following PACS deployment, and a 3-month period 1 year after PACS implementation). Data were obtained for three exam types: chest radiographs, abdominal CT, and spine MRI. The mean dictation turnaround times obtained during the different pre- and post-PACS periods were compared using analysis of variance (ANOVA). Productivity was also determined for each period and for each exam type, and was expressed as the number of studies interpreted per full-time equivalent (FTE) radiologist. In the immediate post-PACS period, dictation turnaround time decreased 20% (p < 0.001) for radiography, but increased 13% (ns) for CT and 28% (p < 0.001) for MRI. One year after PACS was implemented, dictation turnaround time decreased 45% (p < 0.001) for radiography and 36% (p < 0.001) for MRI. For CT, 1 year post-PACS, turnaround times returned to pre-PACS levels. Productivity in the immediate post-PACS period increased 3% and 38% for radiography and CT, respectively, whereas a 6% decrease was observed for MRI. One year after implementation, productivity increased 27%, 98%, and 19% in radiography, CT, and MRI, respectively. PACS benefits, namely, shortened dictation turnaround time and increased productivity, are evident 1 year after PACS implementation. In the immediate post-PACS period, results vary with the different imaging modalities.     

Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography

A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A(z)) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A(z) is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into region scores. The system performance does not significantly differ from that of the observers, when the perihilar regions are excluded from evaluation, and reaches A(z) = 0.85 for the system, with A(z) = 0.88 for both observers.     

基于柔性形态学与聚类算法的X光胸片肺部分割

近年来,在医学领域里,计算机辅助诊断(CAD)技术以其高效的实用性已备受人们关注。对于X光胸片来说,肺部区域的分割是计算机辅助检测胸腔疾病的首要步骤。本研究提出一种自动的肺部分割方法。首先采用柔性形态学滤波进行初始分割,然后用结合先验知识的聚类算法进行二次分割。通过在两组不同图像(计算机X线摄影、传统X光胶片)中仿真测试,平均灵敏度和特异性可达89.39%和92.82%,该方法在保持高分割灵敏度的同时,提高了分割的特异性。     

移动CR与移动DR在床旁胸部摄影中的应用比较

目的对比研究移动计算机X射线摄影（CR）与移动数字化X射线摄影（DR）在床旁胸部摄影中的临床应用价值。方法随机抽取移动CR与移动DR床旁胸部摄影胸片各200张,对2种摄影方式所摄胸片的图像质量及胸内各结构的显示进行对比研究。移动CR和移动DR各200张床旁胸部摄影胸片,以其CT检查为＂金标准＂进行对照,将两组床旁胸片显示的病灶清晰程度分为清晰、可见、模糊、隐约可见、未见5类。统计2组的例数,绘制接受者操作特征（ROC）曲线。结果图像质量：移动CR床旁胸部摄影所得胸片的甲级片率69.0%,乙级片率24.5%,丙级片率5.0%,废片率1.5%。移动DR床旁胸部摄影所得胸片的甲级片率83%,乙级片率16%,丙级片率1%,废片率0。对胸内各结构的显示：移动CR与移动DR床旁胸部摄影所得胸片对胸内各结构的显示率移动CR低于移动DR。对病灶的显示能力：200张移动CR胸片中,126例行CT检查,67例CT所显示的病灶中,移动CR能显示64例,3例未见病灶。200张移动DR胸片中,108例行CT检查,53例CT所显示的病灶中,移动DR能显示52例,1例未见病灶。移动CR与移动DR床旁胸部摄影ROC曲线下的面积分别为0.833和0.918。结论移动CR与移动DR床旁胸部摄影,移动DR摄影的影像质量、对胸内结构的显示及对病灶的显示能力均优于移动CR,在床旁胸部摄影中移动DR具有更高的应用价值。