Computerized analysis of interstitial infiltrates on chest radiographs: a new scheme based on geometric pattern features and Fourier analysis

Computer-assisted detection of infectious lung diseases: a review

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features

Accurate detection of diffuse lung disease is an important step for computerized diagnosis and quantification of this disease. It is also a difficult clinical task for radiologists. We developed a computerized scheme to assist radiologists in the detection of diffuse lung disease in multi-detector computed tomography (CT). Two radiologists selected 31 normal and 37 abnormal CT scans with ground glass opacity, reticular, honeycombing and nodular disease patterns based on clinical reports. The abnormal cases in our database must contain at least an abnormal area with a severity of moderate or severe level that was subjectively rated by the radiologists. Because statistical texture features may lack the power to distinguish a nodular pattern from a normal pattern, the abnormal cases that contain only a nodular pattern were excluded. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. The lungs were first segmented in each slice by use of a thresholding technique, and then divided into contiguous volumes of interest (VOIs) with a 64 x 64 x 64 matrix size. For each VOI, we determined and employed statistical texture features, such as run-length and co-occurrence matrix features, to distinguish abnormal from normal lung parenchyma. In particular, we developed new run-length texture features with clear physical meanings to considerably improve the accuracy of our detection scheme. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by the use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. We investigated the impact of new and conventional texture features, VOI size and the dimensionality for regions of interest on detecting diffuse lung disease. When we employed new texture features for 3D VOIs of 64 x 64 x 64 voxels, our system achieved the highest performance level: a sensitivity of 86% and a specificity of 90% for the detection of abnormal VOIs, and a sensitivity of 89% and a specificity of 90% for the detection of abnormal cases. Our computerized scheme would be useful for assisting radiologists in the diagnosis of diffuse lung disease.

Computer-aided diagnosis in chest radiography

We have developed computer-aided diagnosis (CAD) schemes for the detection of lung nodules, interstitial lung diseases, interval changes, and asymmetric opacities, and also for the differential diagnosis of lung nodules and interstitial lung diseases on chest radiographs. Observer performance studies indicate clearly that radiologists' diagnostic accuracy was improved significantly when radiologists used a computer output in their interpretations of chest radiographs. In addition, the automated recognition methods for the patient and the projection view by use of chest radiographs were useful for integrating the chest CAD schemes into the picture-archiving and communication system (PACS).

[Comparison of LCD and CRT monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic analysis]

Soft copy reading of digital images has been practiced commonly in the PACS environment. In this study, we compared liquid-crystal display (LCD) and cathode-ray tube (CRT) monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic (ROC) analysis. Digital chest images with a 1000x1000 matrix size and a 8 bit grayscale were displayed on LCD/CRT monitor with 2M pixels in each observer test. Eight and ten radiologists participated in the observer tests for detection of nodules and interstitial diseases, respectively. In each observer test, radiologists marked their confidence levels for diagnosis of pulmonary nodules or interstitial diseases. The detection performance of radiologists was evaluated by ROC analyses. The average Az values (area under the ROC curve) in detecting pulmonary nodules with LCD and CRT monitors were 0.792 and 0.814, respectively. In addition, the average Az values in detecting interstitial diseases with LCD and CRT monitors were 0.951 and 0.953, respectively. There was no statistically significant difference between LCD and CRT for both detection of pulmonary nodules (P=0.522) and interstitial lung diseases (P=0.869). Therefore, we believe that the LCD monitor instead of the CRT monitor can be used for the diagnosis of pulmonary nodules and interstitial lung diseases in digital chest images.

Computer-aided diagnosis in chest radiography: a survey

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

Diagnóstico auxiliado por computador na radiologia

Vários desenvolvimentos tecnológicos estão convergindo de forma a aumentar a influência da área de imagens nas pesquisas biomédicas e na medicina clínica. Muitos pesquisadores têm trabalhado no desenvolvimento de sistemas computadorizados para detecção automatizada e quantificação de anormalidades em imagens radiológicas. Estes sistemas são dedicados ao diagnóstico auxiliado por computador. Este artigo discute os conceitos básicos relacionados ao diagnóstico auxiliado por computador e apresenta uma revisão bibliográfica sobre o assunto.

Computer-aided diagnosis for detection of interstitial opacities on chest radiographs

OBJECTIVE

Our objective was to evaluate the impact of a computer-aided diagnostic scheme on radiologists' interpretations of chest radiographs with interstitial opacities by performing an observer test using receiver operating characteristic (ROC) analysis.

MATERIALS AND METHODS

Twenty chest radiographs with normal findings and 20 chest radiographs with abnormal findings were used. Each radiograph was divided into four quadrants. One hundred twenty-nine quadrants (80 normal and 49 abnormal quadrants) were used for testing because we excluded 31 equivocal quadrants. Sixteen independent observers (10 residents and six attending radiologists) participated in this study. The radiologists' performance without and with computer assistance, which indicated cases with normal and abnormal findings by various markers, was evaluated by ROC analysis.

RESULTS

The diagnostic accuracy of the observers improved by a statistically significant magnitude when computer-aided diagnosis was used. Thus, the values for the area under the ROC curve obtained with and without the computer-aided diagnostic output were .970 and .948 (p = .0002), respectively, for all observers; .969 and .943 (p = .0006), respectively, for the residents' subgroup; and .972 and .960 (p = .162), respectively, for the attending radiologists' subgroup. The value for the area under the ROC curve for the computerized scheme by itself was .943.

CONCLUSION

Our computer-aided diagnostic scheme can assist radiologists in the diagnosis or exclusion of interstitial disease on chest radiographs.

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 Az 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.