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H MacMahon

Publications and source records attributed to H MacMahon.

At least 19 recordsLinked to original sources
PubMed1999

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.

Databases as Topic↗
PubMed1999

Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching.

The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.

Humans↗
PubMed1999

Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease.

RATIONALE AND OBJECTIVES: The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease. MATERIALS AND METHODS: The authors used three-layer, feed-forward ANNs with a back-propagation algorithm. The ANNs were designed to distinguish between 11 interstitial lung diseases on the basis of 10 clinical parameters and 16 radiologic findings extracted by chest radiologists. Thus, the ANNs consisted of 26 input units and 11 output units. One hundred fifty actual clinical cases, 110 cases from previously published articles, and 110 hypothetical cases were used for training and testing the ANNs by using a round-robin (or leave-one-out) technique. ANN performance was evaluated with receiver operating characteristic (ROC) analysis. RESULTS: The Az (area under the ROC curve) obtained with actual clinical cases was 0.947, and both the sensitivity and specificity of the ANNs were approximately 90% in terms of indicating the correct diagnosis with the two largest output values among the 11 diseases. CONCLUSION: ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.

Adolescent↗
PubMed1999

Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs.

OBJECTIVE: We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy. MATERIALS AND METHODS: The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale. RESULTS: When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output. CONCLUSION: An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.

Diagnosis, Differential↗
PubMed1998

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.

Humans↗
PubMed1998

Computerized delineation and analysis of costophrenic angles in digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for delineating the costophrenic angles in digital posteroanterior chest radiographs to derive quantitative information that allows for detection of abnormal blunting of the costophrenic angle. MATERIALS AND METHODS: An automated lung-segmentation scheme was used, and small regions of interest were placed in the approximate position of the costophrenic angles in 600 clinical posteroanterior chest radiographs to define a subimage for further analysis. The diaphragmatic aspect of the costophrenic angle was delineated based on column-wise contrast information, and the costal aspect was delineated based on row-wise gray-level maxima. The angle formed by the convergence of these two aspects provided the basis for assessing abnormality. Curve fitting was then performed on these segments to form a continuous costophrenic angle delineation. RESULTS: The computer-determined angles for 1,166 hemithoraces were compared with independent diagnostic assessments by a radiologist. An encouraging level of agreement was found between these two measurements, with the area under the receiver operating characteristic curve attaining a value of 0.83. CONCLUSION: This delineation method enhances the automated lung-segmentation scheme. Quantitative information obtained from the costophrenic angles can be used for automatic evaluation of the presence of costophrenic angle blunting, which may indicate the presence of pleural effusion.

Area Under Curve↗
PubMed1998

Automated lung segmentation in digitized posteroanterior chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed and tested a gray-level thresholding-based approach to automated lung segmentation in digitized posteroanterior chest radiographs. MATERIALS AND METHODS: Gray-level histogram analysis was initially performed to establish a range of thresholds for use during an iterative global gray-level thresholding technique. Local gray-level threshold analysis was then performed on the output of global thresholding. The resulting contours were subjected to several smoothing processes, including a rolling-ball technique. The final contours closely approximated the boundaries of the aerated lung regions. The method was applied to a database of 600 posteroanterior chest images. Radiologists rated the accuracy and completeness of the contours with a five-point scale. RESULTS: Results of the subjective rating evaluation indicated that this method was accurate, with 79% of the assigned ratings reflecting moderately or highly accurate segmentation and only 8% of the ratings indicating moderately or highly inaccurate segmentation. CONCLUSION: This gray-level thresholding-based approach provides accurate automated lung segmentation in digital posteroanterior chest radiographs.

Algorithms↗
PubMed1998

Automated lung segmentation in digital lateral chest radiographs.

We are developing a fully automated computerized scheme for segmenting the lung fields in digital lateral chest radiographs. Existing computer-aided diagnostic (CAD) schemes and automated lung segmentation methods have focused exclusively on the posteroanterior view, despite the diagnostic importance of the lateral view. Information from the lateral radiograph is routinely incorporated by radiologists in their decision-making process, and thus computer analysis of lateral images may potentially add another dimension to current CAD schemes. Automated analysis of the lung fields in lateral images will necessarily require accurate segmentation. Our scheme employs an initial procedure to eliminate external and subcutaneous pixels. Global gray-level histogram analysis then allows for the identification of a range of gray-level thresholds. An iterative gray-level thresholding scheme is implemented using this range of thresholds to construct a series of binary images in which contiguous regions are identified and geometrically analyzed. Regions determined to be outside the lungs are prevented from contributing to binary images at later iterations. Adaptive local gray-level thresholding is applied along the initial contour that results from the global thresholding procedure to extend the contour closer to the true lung borders. This local thresholding method uses regions of interest of various dimensions, depending on the enclosed anatomy. Smoothing and polynomial curve fitting complete the segmentation. A database of 100 normal and 100 abnormal lateral images was analyzed. Quantitative comparison of computer-segmented lung regions with lung regions manually delineated by two radiologists indicated that 83% and 84% of normal and abnormal images, respectively, displayed segmentation contours within three standard deviations of the mean inter-radiologist contour degree-of-overlap value.

Automation↗
PubMed1998

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.

Adolescent↗
PubMed1997

Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.

We devised an automated classification scheme by using the rule-based method plus artificial neural networks (ANN) for distinction between normal and abnormal lungs with interstitial disease in digital chest radiographs. Four measures used in the classification scheme are determined from the texture and geometric-pattern feature analyses. The rms variation and the first moment of the power spectrum of lung patterns are determined as measures for the texture analysis. In addition, the total area of nodular opacities and the total length of linear opacities are determined as measures for the geometric-pattern feature analysis. In our classification scheme with these measures, we identify obviously normal and abnormal cases first by the rule-based method and then ANN is applied for the remaining difficult cases. The rule-based plus ANN method provided a sensitivity of 0.926 at the specificity of 0.900, which was considerably improved compared to performance of either the rule-based method alone or ANNs alone.

Diagnosis, Computer-Assisted↗
PubMed1997

Automated registration of ventilation-perfusion images with digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors have developed an automated computerized technique for registering radionuclide lung scan images with digital chest radiographs. METHODS: Threshold analysis was used to construct contours around the high-activity regions of radionuclide ventilation-perfusion images. Analogous contours were constructed around the lung regions of the corresponding digitized radiographs. Contour dimensions and anatomic landmark locations were then used to superimpose the radiographic, ventilation, and perfusion images. RESULTS: Evaluation of 25 sets of images indicated that the scheme provided adequate to excellent registration in 91% of the pairwise combinations. CONCLUSION: This automated scheme for registering ventilation-perfusion images with digital chest radiographs has the potential to aid radiologists in the interpretation of these images.

Adult↗
PubMed1997

Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis.

We have been developing automated computerized schemes to assist radiologists in interpreting chest radiographs for interstitial disease based on texture analysis and geometric-pattern feature analysis. In this study, we attempted to improve the performance of the geometric-pattern feature analysis, because the current classification performance with geometric-pattern feature analysis is considerably lower than that of texture analysis. In order to improve the performance in distinguishing between normal lungs and abnormal lungs with interstitial disease, we attempted to remove rib edges in regions of interest (ROIs) by using an edge detection technique, and also to reduce false positives by using feature analysis techniques. In addition, the effects of many parameters on classification performance were investigated to identify proper threshold levels, and subsequently the specificity of the geometric-pattern feature analysis was improved from 69.5% to 86.1% at a sensitivity of 95.0%. Using a combined rule-based method with texture analysis and geometric-pattern feature analysis plus the artificial neural network (ANN) method for classification, a high specificity of 96.1% was obtained at a sensitivity of 95.0%.

Biophysical Phenomena↗
PubMed1997

Development of an improved CAD scheme for automated detection of lung nodules in digital chest images.

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.

Adult↗
PubMed1997

Digital chest radiography: effect of temporal subtraction images on detection accuracy.

PURPOSE: To improve early detection of disease in chest radiographs, the authors developed a digital processing technique that geometrically warps and subtracts a previous radiograph from a current radiograph to produce a temporal subtraction image. An observer test was performed to evaluate the effects of the temporal subtraction image technique on detection of interval change. MATERIALS AND METHODS: Fifty pairs of chest radiographs, including a baseline examination and a subsequent radiograph, were selected (25 cases in which potentially important new abnormalities had developed, and 25 in which there was no interval change). The baseline examination was chosen from multiple prior radiographs to minimize initial misregistration. By means of receiver operating characteristic (ROC) analysis, the ability of 11 observers to detect pathologic change when viewing the paired digitized baseline and subsequent radiographs was compared with their ability when viewing the same paired radiographs together with temporal subtraction images. Positive cases demonstrated focal new abnormalities that were greater than 1 cm in diameter. RESULTS: The mean area (Az) under the ROC curves increased from 0.89 without to 0.98 with the temporal subtraction images. When the paired digitized previous and current chest radiographs were viewed in conjunction with the temporal subtraction images, a significant improvement in detection of new abnormalities was achieved (P = .00004), whereas the mean interpretation time was reduced by 19.3% (from 52 to 42 seconds, including the time to record the score and to move to the next case) (P = .0019). CONCLUSION: The temporal subtraction technique can significantly improve sensitivity and specificity for detection of interval change in chest radiographs.

Humans↗
PubMed1996

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 x 128 matrix size (22.4 mm x 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.

Humans↗
PubMed1996

The effect of x-ray beam alignment on the performance of antiscatter grids.

While the qualitative effects of grid misalignment are known, we have quantified the effect of different degrees of grid misalignment on image contrast and patient exposure. Radiographs were made of a phantom consisting of five lead disks on top of a 15 cm block of lucite. Four 60 lines/cm grids, having grid ratios of 3:1, 4:1, 6:1, and 8:1 were used. When the tube was angled more than three degrees across the grid lines, the contrast improvement factor decreased substantially for all four grids, as much as 46% for an 8:1 grid with a 12 degrees misalignment. There was a concomitant decrease in film optical density, which if compensated for by an increase in patient exposure, would lead to a higher effective bucky factor. With the exception of the 3:1 grid, if the grid is misaligned by more than 6 degrees, higher signal-to-noise ratios can be attained by removing the grid and using the increased patient exposure to reduce noise.

Colloids↗
PubMed1996

Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.

PURPOSE: To evaluate the effect of a computer-aided diagnosis (CAD) scheme on radiologists' performance in the detection of lung nodules, and to examine a new method of receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: One hundred twenty radiographs (60 normal and 60 abnormal with lung nodules of varying subtlety) were used. Sixteen radiologists (two thoracic, six general, and eight residents) participated in an observer study in which they read both conventional radiographs and digitized radiographs. The radiologists' performance was evaluated with ROC analysis with two different methods (independent testing and sequential testing) and a continuous rating scale. RESULTS: Az (area under the best fit binormal ROC curve when it is plotted in the unit square) values obtained from ROC analysis with and without CAD output were 0.940 and 0.894, respectively, in the independent test and 0.948 and 0.906, respectively, in the sequential test. Findings with both methods indicated that the CAD scheme statistically significantly improved diagnostic accuracy, particularly for radiologists with less experience (P < .001). Reading time was not increased when CAD was used. CONCLUSION: The CAD scheme can assist radiologists in the detection of lung nodules on chest radiographs.

Adult↗