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.     

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.     

Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease

The authors have developed an automated computeraided diagnostic (CAD) scheme by using artificial neural networks (ANNs) on quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs for distinguishing between normal and abnormal patterns. For training and testing of the second ANN, the vertical output patterns obtained from the 1st ANN were used for each ROI. The output value of the second ANN was used to distinguish between normal and abnormal ROIs with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a specified threshold level, the image was classified as abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images. The combination of the rule-based method and the third ANN also was applied to the classification between normal and abnormal chest images. The performance of the ANNs was evaluated by means of receiver operating characteristic (ROC) analysis. The average A_z value (area under the ROC curve) for distinguishing between normal and abnormal cases was 0.976±0.012 for 100 chest radiographs that were not used in training of ANNs. The results indicate that the ANN trained with image data can learn some statistical properties associated with interstitial infiltrates in chest radiographs.     

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.     

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.     

Biplane Correlation Imaging: A Feasibility Study Based on Phantom and Human Data

The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior–anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.     

Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields

We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature-extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true-positive rates and low false-positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.     

True Detection Versus “Accidental” Detection of Small Lung Cancer by a Computer-Aided Detection (CAD) Program on Chest Radiographs

To evaluate the number of actual detections versus “accidental” detections by a computer-aided detection (CAD) system for small nodular lung cancers (≤30 mm) on chest radiographs, using two different criteria for measuring performance. A Food-and-Drug-Administration-approved CAD program (version 1.0; Riverain Medical) was applied to 34 chest radiographs with a “radiologist-missed” nodular cancer and 36 radiographs with a radiologist-mentioned nodule (a newer version 3.0 was also applied to the 36-case database). The marks applied by this CAD system consisted of 5-cm-diameter circles. A strict “nodule-in-center” criterion and a generous “nodule-in-circle” criterion were compared as methods for the calculation of CAD sensitivity. The increased sensitivities by the nodule-in-circle criterion were considered as nodules detected by chance. The number of false-positive (FP) marks was also analyzed. For the 34 radiologist-missed cancers, the nodule-in-circle criterion caused eight more cancers (24%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results. For the 36 radiologist-mentioned nodules, the nodule-in-circle criterion caused seven more lesions (19%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results, and three more lesions (8%) to be detected by chance when using the version 3.0 results. Version 1.0 yielded a mean of six FP marks per image, while version 3.0 yielded only three FP marks per image. The specific criteria used to define true- and false-positive CAD detections can substantially influence the apparent accuracy of a CAD system.     

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.     

Image feature analysis and computer-aided diagnosis in digital radiography: automated detection of pneumothorax in chest images.

In order to aid radiologists in the diagnosis of pneumothorax from chest radiographs, an automated method for detection of subtle pneumothorax is being developed. The computerized method is based on the detection of a fine curved-line pattern, which is a unique feature of radiographic findings of pneumothorax. Initially, regions of interest (ROIs) are determined in each upper lung area, where subtle pneumothoraces commonly appear. The pneumothorax pattern is enhanced by the selection of edge gradients within a limited range of orientations. Rib edges included in this edge-enhanced image are removed, based on the locations of posterior ribs that are determined separately. A subtle curved line due to pneumothorax is then detected by means of the Hough transform. The detected pneumothorax pattern is marked on the chest image displayed on a CRT monitor. With the present computer method applied to 50 chest images (28 normals and 22 abnormals with pneumothorax), we were able to detect 77% of pneumothoraces, with 0.44 false-positives per image.     

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.     

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.     

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.     

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.     

Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections

Currently, radiologists can fail to detect lung nodules in up to 30% of actually positive cases. If a computerized scheme could alert the radiologist to locations of suspected nodules, then potentially the number of missed nodules could be reduced. We are developing such a computerized scheme that involves a difference-image approach and various feature-extraction techniques. In this paper, we describe our use of digital morphological processing in the reduction of computer-identified false-positive detections. A feature-extraction technique, which includes the sequential application of nonlinear filters of erosion and dilation, is employed to reduce the camouflaging effect of ribs and vessels on nodule detection. This additional feature-extraction technique reduced the true-positive rate of the computerized scheme by 13% and the false-positive rate by 50%. In a comparison of the scheme with and without the additional feature-extraction technique, inclusion of the additional technique increased the detection sensitivity by about half at the level of three to four false-positive detections per chest image.     

Multiview-based computer-aided detection scheme for breast masses

In this study, we developed and tested a new multiview-based computer-aided detection (CAD) scheme that aims to maintain the same case-based sensitivity level as a single-image-based scheme while substantially increasing the number of masses being detected on both ipsilateral views. An image database of 450 four-view examinations (1800 images) was assembled. In this database, 250 cases depicted malignant masses, of which 236 masses were visible on both views and 14 masses were visible only on one view. First, we detected suspected mass regions depicted on each image in the database using a single-image-based CAD. For each identified region (with detection score > or = 0.55), we then identified a matching strip of interest on the ipsilateral view based on the projected distance to the nipple along the centerline. By lowering CAD operating threshold inside the matching strip, we searched for a region located inside the strip and paired it with the original region. A multifeature-based artificial neural network scored the likelihood of the paired "matched" regions representing true-positive masses. All single (unmatched) regions except for those either with very high detection scores (> or = 0.85) or those located near the chest wall that cannot be matched on the other view were discarded. The original single-image-based CAD scheme detected 186 masses (74.4% case-based sensitivity) and 593 false-positive regions. Of the 186 identified masses, 91 were detected on two views (48.9%) and 95 were detected only on one view (51.1%). Of the false-positive detections, 54 were paired on the ipsilateral view inside the corresponding matching strips and the remaining 485 were not, which represented 539 case-based false-positive detections (0.3 per image). Applying the multiview-based CAD scheme, the same case-based sensitivity was maintained while cueing 169 of 186 masses (90.9%) on both views and at the same time reducing the case-based false-positive detection rate by 23.7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate.     

Assessment of Performance and Reliability of Computer-Aided Detection Scheme Using Content-Based Image Retrieval Approach and Limited Reference Database

Content-based image retrieval approach was used in our computer-aided detection (CAD) schemes for breast cancer detection with mammography. In this study, we assessed CAD performance and reliability using a reference database including 1500 positive (breast mass) regions of interest (ROIs) and1500 normal ROIs. To test the relationship between CAD performance and the similarity level between the queried ROI and the retrieved ROIs, we applied a set of similarity thresholds to the retrieved similar ROIs selected by the CADschemes for all queried suspicious regions, and used only the ROIs that were above the threshold for assessing CAD performance at each threshold level. Using the leave-one-out testing method, we computed areas under receiver operating characteristic (ROC) curves (A _Z ) to assess CAD performance. The experimental results showed that as threshold increase, (1) less true positive ROIs can be referenced in the database than normal ROIs and (2) the A _Z value was monotonically increased from 0.854±0.004 to 0.932±0.016. This study suggests that (1) in order to more accurately detect and diagnose subtle masses, a large and diverse database is required, and (2) assessing the reliability of the decision scores based on the similarity measurement is important in application of the CBIR-based CAD schemes when the limited database is used.     

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.     

Computer-Aided Nodule Detection on Digital Chest Radiography: Validation Test on Consecutive T1 Cases of Resectable Lung Cancer

Purpose To evaluate the usefulness of a commercially available computer-assisted diagnosis (CAD) system on operable T1 cases of lung cancer by use of digital chest radiography equipment. Materials and Methods Fifty consecutive patients underwent surgery for primary lung cancer, and 50 normal cases were selected. All cancer cases were histopathologically confirmed T1 cases. All normal individuals were selected on the basis of chest computed tomography (CT) confirmation and were matched with cancer cases in terms of age and gender distributions. All chest radiographs were obtained with one computed radiography or two flat-panel detector systems. Eight radiologists (four chest radiologists and four residents) participated in observer tests and interpreted soft copy images by using an exclusive display system without and with CAD output. When radiologists diagnosed cases as positives, the locations of lesions were recorded on hard copies. The observers’ performance was evaluated by receiver operating characteristic analysis. Results The overall detectability of lung cancer cases with CAD system was 74% (37/50), and the false-positive rate was 2.28 (114/50) false positives per case for normal cases. The mean A_z value increased significantly from 0.896 without CAD output to 0.923 with CAD output (P = 0.018). The main cause of the improvement in performance is attributable to changes from false negatives without CAD to true positives with CAD (19/31, 61%). Moreover, improvement in the location of the tumor was observed in 1.5 cases, on average, for radiology residents. Conclusion This CAD system for digital chest radiographs is useful in assisting radiologists in the detection of early resectable lung cancer.     

Differentiation between nodules and end-on vessels using a convolution neural network architecture

In recent years, many computer-aided diagnosis schemes have been proposed to assist radiologists in detecting lung nodules. The research efforts have been aimed at increasing the sensitivity while decreasing the false-positive detections on digital chest radiographs. Among the problems of reducing the number of false positives, the differentiation between nodules and end-on vessels is one of the most challenging tasks performed by computer. Most investigators have used a conventional two-stage pattern recognition approach, ie, feature extraction followed by feature classification. The performance of this approach depends totally on good feature definition in the feature extraction stage. Unfortunately, suitable feature definition and corresponding extraction implementation algorithms proved to be very difficult to define and specify. A convolution neural network (CNN) architecture, trained by direct connection to the raw image is proposed to tackle the problem. The CNN, which uses locally responsive activation function, is directly and locally connected to the raw image. The performance of the CNN is evaluated in comparison to an expert radiologist. We used the receiver operating characteristics (ROC) method with area under the curve (A_z) as the performance index to evaluate all the simulation results. The CNN showed superior performance (A_z=0.99) to the radiologist’s (A_z=0.83). The CNN approach can potentially be applied to other applications, such as the differentiation of film defects and microcalcifications in mammography, in which the image features are difficult to define or not known a priori.