Automatic segmentation of lung fields in chest radiographs

The delineation of important structures in chest radiographs is an essential preprocessing step in order to automatically analyze these images, e.g., for tuberculosis screening support or in computer assisted diagnosis. We present algorithms for the automatic segmentation of lung fields in chest radiographs. We compare several segmentation techniques: a matching approach; pixel classifiers based on several combinations of features; a new rule-based scheme that detects lung contours using a general framework for the detection of oriented edges and ridges in images; and a hybrid scheme. Each approach is discussed and the performance of nine systems is compared with interobserver variability and results available from the literature. The best performance is obtained by the hybrid scheme that combines the rule-based segmentation algorithm with a pixel classification approach. The combinations of two complementary techniques leads to robust performance; the accuracy is above 94% for all 115 images in the test set. The average accuracy of the scheme is 0.969 +/- 0.0080, which is close to the interobserver variability of 0.984 +/- 0.0048. The methods are fast, and implemented on a standard PC platform.     

Computerized analysis of abnormal asymmetry in digital chest radiographs: Evaluation of potential utility

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.     

Development of a digital duplication system for portable chest radiographs

To provide high-quality duplicate chest images for the intensive care units, we have developed a digital duplication system in which film digitization is performed in conjunction with nonlinear density correction, contrast adjustment, and unsharp mask filtering. This system provides consistent image densities over a wide exposure range and enhancement of structures in the mediastinum and upper abdominal areas, improving visibility of catheters and tubes. The image quality is often superior to that of the original radiograph and is more consistent from day to day. Repeat rates for portable chest radiographs have been reduced by more than a factor of two since implementation of digitization in December 1991, and the number of repeat examinations caused by exposure errors have been substantially reduced.     

Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: Preliminary results

We are developing a computerized method for detection and characterization of interstitial diseases based on a quantitative analysis of geometric features of various infiltrate patterns in digital chest radiographs. In our approach, regions of interest (ROIs) with 128 × 128 matrix size (22.4 mm × 22.4 mm) are automatically selected, covering peripheral lung regions. Next, nodular and linear opacities, which are the basic components of interstitial infiltrates, are identified from two processed images obtained by use of a multiple-level thresholding technique and a line enhancement filter, respectively. Finally, the total area of nodular opacities and the total length of linear opacities in each ROI are determined as measures of geometric pattern features. We have applied this computer analysis to 72 ROIs with normal and abnormal patterns that were classified in advance by six chest radiologists. Preliminary results indicate that the distribution of measures of geometric-pattern features correlate well with radiologists’ classification. These early results are encouraging, and further evaluation hopes to establish that this computerized method might prove useful to radiologists in their assessment of interstitial diseases.     

Image feature analysis and computer-aided diagnosis in digital radiography: effect of digital parameters on the accuracy of computerized analysis of interstitial disease in digital chest radiographs

We are developing a computerized method for measurement of lung texture in digital chest radiographs for detection and characterization of interstitial disease. Physical texture measures are obtained from analysis of the power spectrum of the lung texture. We have investigated the effect of digital parameters such as pixel size, regions of interest size, the number of quantitation levels, and the peak frequency of the visual system response, as well as the effect of the unsharp masking technique on the performance of this computerized method. We calculated the texture measures by changing digital parameters for 100 normal lungs and 100 abnormal lungs in our database. Receiver operating characteristic (ROC) curves were employed for evaluation of the performance of this computerized method for distinguishing between normal and abnormal lungs. We used the area under the ROC curve to compare the detection accuracy for interstitial infiltrates. We believe that the results of this study may be useful as a guide in the design of computerized schemes for lung texture analysis in digital chest radiographs.     

Image feature analysis and computer-aided diagnosis in digital radiography: detection and characterization of interstitial lung disease in digital chest radiographs

We are developing an automated method for determining physical measures of lung textures in digital chest radiographs in order to detect and characterize interstitial lung disease. With this method, the underlying background density variations caused by the gross lung and chest wall anatomy are corrected for in order to isolate the fluctuating patterns of the underlying lung texture for subsequent computer analysis. The power spectrum of lung texture, which is obtained from the two-dimensional Fourier transform, is filtered by the visual system response of the human observer. The magnitude and coarseness (or fineness) of the lung textures are then quantified by the root-mean-square (rms) variation and the first moment of the power spectrum, respectively. Preliminary results indicate that the rms variations and/or the first moments of the texture of abnormal lungs with various interstitial diseases are clearly different from those of normal lungs. Our results suggest strongly that quantitative texture measures calculated from digital chest images may be useful to radiologists in their assessment of interstitial disease.     

Development of a randomised contrast detail digital phantom for observer detectability study

The accuracy and the efficacy of radiological diagnosis depend, to a large extent, on the conditions under which radiographs and images are viewed. This mainly involves the luminance of the display devices and the ambient room illumination. We report a perceptual study to investigate the relationship between detectability and monitor luminance as well as ambient illuminance. A statistical test pattern was used in this study, and the test pattern was developed using Microsoft® Visual Basic 6. The test pattern contained a set of randomised contrast detail objects, that is, disks of different diameters (0.7, 1.0, 1.4, and 2.0 mm) and contrasts against a black background (2.7, 3.9, 5.5, and 7.8%), simulating lesions in digital images. The receiver operating characteristic (ROC) analysis was used in this study. The results indicated that a set of optimal viewing conditions exists and that it has a significant effect on detectability performance.     

数字X射线摄影机床边胸部摄影时周围散射线量测量

目的测量移动式数字X射线摄影（DR）机床边胸部摄影时照射野外的散射线辐射剂量。方法采用移动式DR机,用80kV、240mA、10ms和110cm焦-片距,垂直照射胸部体模,将测量仪分别放在360°均等分的8个角度、距照射野外1m和2m处,共测16个点。结果在0°和180°2m远距离处,测量仪上散射线量指示值最大,均为0.014μSv。在90°和270°2m远距离处最小,分别为0.011μSv和0.010μSv。结论一次移动式DR床边机胸部摄影,如患者和工作人员距照射野外2m远,其散射线辐射剂量低于人体7min所接受的自然本底照射剂量,不会产生危害后果。     

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.     

Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs

A novel contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities on a single chest radiograph. With this method, the lateral inclination is first corrected by rotating and shifting the original chest image so that the midline of the thorax is aligned with the vertical centerline of the original chest image. The rotated image is then flipped laterally to produce a reversed "mirror" image. Finally, the mirror image is warped and subtracted from the original image for derivation of the contralateral subtraction image. The three key techniques which are employed in this study are applied successively to the initial contralateral subtraction technique for acquisition of improved subtraction images. One hundred PA chest radiographs, including 50 normals and 50 abnormals, were used as the database for this study. The percentage of chest images, which were rated as being adequate, good, or excellent quality of subtraction images by employing a subjective evaluation method, was improved from 73% to 91% by use of the three key techniques. The contralateral subtraction technique can be used for detection of any asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on a single chest radiograph, and therefore has potential utility in a high proportion of abnormal cases.     

Projection profile analysis for automated detection of abnormalities in chest radiographs

Abnormalities in chest images often present as abnormal opacity or abnormal asymmetry. We have developed a novel method for automated detection of abnormalities in chest radiographs by use of these features. Our method is based on an analysis of the projection profile obtained by projecting the pixels data of a frontal chest image on to the mediolateral axis. Two indices, lung opacity index and lung symmetry index, are computed from the projection profile. Lung opacity index and lung symmetry index are then combined to detect gross abnormalities in chest radiographs. The values of lung opacity index are found to be 0.38 +/- 0.05 and 0.37 +/- 0.06 for normal right and left lung, respectively. The values of lung symmetry index are found to be 0.018 +/- 0.014 for normal chest images. The discrimination for the combination of the two indices is evaluated by linear discriminant analysis and receiver operating characteristic (ROC) analysis. Area Az under the ROC curve with the combination of the two indices in the classification of normal and abnormal chest images is 0.963.     

An automatic method for enhancing the display of different tissue densities in digital chest radiographs

Digital chest radiographs are often too bright and/or lack contrast when viewed on a video display. This often occurs in radiographs taken of patients with dense lungs, or when incorrect x-ray exposure techniques or inappropriate image preprocessing operations are performed (eg, by the computed radiography system or laser scanner). This article describes a method to automatically provide brightness and contrast adjustments to selectively enhance either soft or dense tissues. This method reduces viewer interaction and improves displayed image quality. The algorithm analyzes the gray-level histogram of a chest radiograph and determines the breakpoints that separate the region outside the patient (background), the radiographically soft tissues, and the radiographically dense tissues. From these breakpoints, a series of piecewise linear look-up tables (LUTs) is generated to selectively enhance either the soft tissues or the dense tissues. This is performed by: (1) varying the contrast in the patient background to achieve the desired overall brightness, (2) selectively increasing the contrast of the tissue region of interest, and (3) reducing or maintaining the contrast of the remaining region. The resulting LUTs are applied to the original image via video display.     

Scatter rejection and low-contrast performance of a slot-scan digital chest radiography system with electronic aft-collimation: a chest phantom study

Anti-scatter grids have been widely used to reject scatter and increase the perceptibility of low-contrast object in chest radiography; however they also attenuate the primary x-rays, resulting in a substantial degradation of primary information. Compensation for this degradation requires the use of higher exposure technique hence higher dose to the patient. A more efficient approach to reject scatter is the slot-scan imaging technique which employs a narrow scanning x-ray fan beam in conjunction with a slit or slot shaped solid state detector or an area detector used with an aft-collimator. With this approach, scatter can be rejected effectively without the need to attenuate primary x-rays. This paper demonstrates an electronic aft-collimation method, referred to as the alternate line erasure and readout (ALER) technique, for implementing the slot-scan digital radiography with a modern flat-panel detector. With this technique, instead of first exposing the detector and then reading the image line by line, the image line on the leading edge of the scanning fan beam is reset to erase the scatter accumulated prior to the arrival of the fan beam x-rays, while the image line on the trailing edge of the scanning fan beam is read out to acquire the image signals following the fan-beam exposure. These reset and readout processes are alternated and repeated as the x-ray fan beam scans across the detector. An anthropomorphic chest phantom was imaged to evaluate the scatter rejection ability and the low-contrast performance for the ALER technique and compare them with those for the anti-scatter grid method in full-field chest imaging. With a projected beam width of 16 mm, the slot-scan/ALER technique resulted in an average reduction of the scatter-to-primary ratios by 81%, 84%, 82%, and 86% versus 65%, 73%, 74%, and 73% with the anti-scatter grid method in the lungs, mediastinum, retrocardium, and subdiaphragm, respectively. The average CNR for the slot-scan/ALER technique was found to improve by 135%, 133%, 176%, and 87% versus 15%, 15%, 38%, and -11% with the anti-scatter grid method in the mediastinum, retrocardium, subdiaphragm, and lungs, respectively. These results demonstrated that the slot-scan/ALER technique can be used to achieve equally effective scatter rejection but substantially higher low-contrast performance than the anti-scatter grid method.     

Temporal subtraction of dual-energy chest radiographs

Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.     

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.     

The futility of universal pre-employment chest radiographs

In a developmental center, a preemployment chest x-ray was required for all job applicants. We scrutinized the pros and cons of this practice through a review of the medical literature and our experience, and discussion with our colleagues. We concluded that such chest x-ray caused unwarranted radiation exposure, did not produce compliance with the tuberculosis laws, gave a false sense of security regarding workers' compensation risk management, was contrary to established occupational medicine practice guidelines, and was unnecessary and wasteful. We discontinued such chest x-rays. The purpose of the pre-employment examination should remain narrowly job related. Even long-established procedures require periodic utilization review.     

Simplified approach for quantitative digital holographic phase contrast imaging of living cells

Many interferometry-based quantitative phase contrast imaging techniques require a separately generated coherent reference wave. This results in a low phase stability and the demand for a precise adjustment of the intensity ratio between object and reference wave. To overcome these problems, the performance of a Michelson interferometer approach for digital holographic microscopy was analyzed that avoids a separately generated reference wave by superposition of different image areas. It is shown that this simplified arrangement yields improved phase stability. Furthermore, results from time-lapse investigations on living pancreas tumor cells demonstrate the capability of the method for reliable quantitative phase contrast imaging.