Potential usefulness of digital imaging in clinical diagnostic radiology: computer-aided diagnosis.
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Angiography↗
Biomedical subjects
M L Giger
Publications and source records attributed to M L Giger.
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 contained 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- x 64-pixel ROIs to smaller 8- x 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 Az 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.
False Positive Reactions↗
Automated segmentation of digitized mammograms.
RATIONALE AND OBJECTIVES: Fast and reliable segmentation of digital mammograms into breast and nonbreast regions is an important prerequisite for further image analysis. We are developing a segmentation algorithm that is fully automated and can operate independent of type of digitizing system, image orientation, and image projection. METHODS: The algorithm identifies unexposed and direct-exposure image regions and generates a border surrounding the valid breast region, which can then be used as input for further image analysis. The program was tested on 740 digitized mammograms; the segmentation results were evaluated by two expert mammographers and two medical physicists. RESULTS: In 97% of the mammograms, the segmentation results were rated as acceptable for use in computer-aided diagnostic schemes. Segmentation problems encountered in the remaining 22 images (2.9%) were most often caused by digitization artifacts or poor mammographic technique. CONCLUSION: The developed algorithm can serve as a component of an "intelligent" workstation for computer-aided diagnosis in mammography.
Algorithms↗
Analysis of spiculation in the computerized classification of mammographic masses.
Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologist's ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.
Automation↗
Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers.
To investigate the performance of a computerized method for the automated detection of clustered microcalcifications in digitized mammograms from a variety of screening centers, the authors invited 118 radiologists to bring up to five mammograms to their scientific exhibit at the 1993 meeting of the Radiological Society of North America (RSNA). Forty-three mammograms from 14 sites were brought to the exhibit, where they were digitized and analyzed. Results of the analysis on the RSNA cases were compared with those obtained on a standard database of 39 mammograms collected from two centers. The performance of the detection algorithm on the RSNA images was lower than that achieved on the standard database. This lower performance was due in part to the higher fraction of very subtle clustered microcalcifications in the RSNA cases, as well as the apparent dependence of the algorithm on image characteristics (eg, contrast and noise), which varied from center to center. The authors conclude that the algorithm is robust and accurate enough to undergo clinical testing. When it is implemented clinically, the computerized scheme must be customized to the image characteristics at each specific screening center to obtain optimal performance.
Breast Diseases↗
Computerized characterization of mammographic masses: analysis of spiculation.
Although general rules for the differentiation between benign and malignant breast lesions exist, only 10 to 20% of masses referred for surgical breast biopsy are actually malignant. We are developing, as an aid to radiologists, a computerized scheme for the classification of masses appearing on mammograms to reduce the number of false-positive diagnoses of malignancies. The classification scheme involves the extraction of the margin of masses in order to quantify the degree of spiculation, which, in turn, is related to the likelihood of malignancy. When two measures of spiculation are used as input to an artificial neural network, the scheme achieves a performance similar to that achieved when radiologist's spiculation ratings alone are used for a clinical database of 53 masses. The computerized classification scheme therefore has the potential to effectively aid radiologists in determining appropriate patient management.
Breast Neoplasms↗
Computerized detection of masses in digital mammograms: investigation of feature-analysis techniques.
Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality. We are developing a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs. Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique, which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study, we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to reduce the number of nonmass (ie, false-positive) detections. These feature-analysis techniques involve (1) the extraction of various features (such as area, contrast, circularity and border-distance based on the density and geometric information of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections. Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated in combination with the computerized detection scheme that uses the nonlinear bilateral-subtraction technique using free-response receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the feature-analysis techniques effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated without loss in sensitivity when the feature-analysis techniques were used.
Breast Neoplasms↗
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.
Computer Systems↗
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 AZ 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.
Diagnosis, Computer-Assisted↗
Computerized detection of pulmonary nodules in computed tomography images.
RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.
Artificial Intelligence↗
Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.
A computer-aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme. The shift-invariant neural network is a multilayer back-propagation neural network with local, shift-invariant interconnections. The advantage of the shift-invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift-invariant neural network was evaluated by means of a jackknife (or holdout) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve (Az) of 0.91. Approximately 55% of false-positive ROIs were eliminated without any loss of the true-positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three-layer, feed-forward neural network. The effect of the network structure on the performance of the shift-invariant neural network is also studied.
Biophysical Phenomena↗
Computerized detection of abnormal asymmetry in digital chest radiographs.
A technique for automated detection of abnormal asymmetry in digital chest radiographs is being developed. Such a method could be used to prescreen chest radiographs to bring obviously abnormal cases to the immediate attention of a radiologist. In addition, this technique may be used to detect large-area abnormalities which may cause other, more lesion-specific computer algorithms to fail. Asymmetric abnormalities are detected by multiple stages of gray-level thresholding. Lung contours are determined, and after a centroid test is used to eliminate contours external to the lungs, the areas of remaining contours are calculated. The present scheme, applied to a database of 70 chest images, yielded a sensitivity of 91% and a specificity of 80%.
Biophysical Phenomena↗
Effect of case selection on the performance of computer-aided detection schemes.
The choice of clinical cases used to train and test a computer-aided diagnosis (CAD) scheme can affect the test results (i.e., error rate). In this study, we deliberately modified the components of our testing database to study the effects of this modification on measured performance. Using a computerized scheme for the automated detection of breast masses from mammograms, it was found that the sensitivity of the scheme ranged between 26% and 100% (at a false positive rate of 1.0 per image) depending on the cases used to test the scheme. Even a 20% change in the cases comprising the database can reduce the measured sensitivity by 15%-25%. Because of the strong dependence of measured performance on the testing database, it is difficult to estimate reliably the accuracy of a CAD scheme. Furthermore, it is questionable to compare different CAD schemes when different cases are used for testing. Sharing databases, creating a common database, or using a quantitative measure to characterize databases are possible solutions to this problem. However, none of these solutions exists or is practiced at present. Therefore, as a short-term solution, it is recommended that the method used for selecting cases, and histograms or mean and standard deviations of relevant image features be reported whenever performance data are presented.
Breast Neoplasms↗
Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique.
An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. In this technique, the breast region is first identified in each digital mammogram by use of histogram analysis and morphological filtering operations. The anterior portions of the tracked breast border and computer-identified nipple positions are selected as landmarks for use in image registration. The paired right and left breast images, either from mediolateral oblique or craniocaudal views, are then registered relative to each other by use of a least-squares matching method. This automated alignment technique has been applied to our computerized detection scheme that employs a nonlinear bilateral-subtraction method for the initial identification of possible masses. The effectiveness of using bilateral subtraction in identifying asymmetries between corresponding right and left breast images is examined by comparing detection performances obtained with various computer-simulated misalignments of 40 pairs of clinical mammograms. Based on free-response receiver operating characteristic and regression analyses, the detection performance obtained with the automated alignment technique was found to be higher than that obtained with simulated misalignments. Detection performance decreased gradually as the amount of simulated misalignment increased. These results indicate that automatic alignment of breast images is possible and that mass-detection performance appears to improve with the inclusion of asymmetric anatomic information but is not sensitive to slight misalignment.
Breast Neoplasms↗
Digital image subtraction of temporally sequential chest images for detection of interval change.
An automated digital image subtraction technique for temporally sequential chest images has been developed in order to aid radiologists in the detection of interval changes. A number of small regions of interest (ROIs) are selected automatically in the lung areas of two temporally sequential chest images. A local matching, based on a cross-correlation method, is performed on each pair of corresponding ROIs in order to determine shift values for the coordinates of two images. A proper warping of x,y coordinates is obtained by fitting two-dimensional polynomials to the distributions of shift values. One of the images is warped and then subtracted from the other. Forty six pairs of chest images (42 with interval changes and 4 without interval change) were processed using this method. The subtraction images were able to enhance various important interval changes, such as differences in the size of tumor masses, changes in heart size, and changes in pulmonary infiltrates or pleural effusions. Approximately 70% of the pairs showed reasonably good registration.
Humans↗
Multifractal radiographic analysis of osteoporosis.
An important complication of osteoporosis is fracture. Alteration in bone structure, as well as decreased bone mass, contribute to the tendency to fracture in osteoporosis. Current methods that measure bone mass alone show substantial overlap of the measurements of osteoporotic patients who fracture with those that do not. Our aim is to develop noninvasive methods of evaluating bone structure on plain film radiographs to better predict fracture risk in osteoporosis. Regions of interest (ROIs) were selected from digitized lateral lumbar spine radiographs of 43 patients being seen in an osteoporosis clinic. The fractal dimension of these ROIs was estimated using a surface area method. The ability of fractal dimension to distinguish between cases that had fracture elsewhere in the spine from those that did not, was evaluated using receiver operating characteristic (ROC) analysis. These results were compared with ROC analysis for these same patients using bone mineral density (BMD) measurements (bone mass). Significantly larger Az (area under ROC curve) values were obtained using fractal dimension (0.87) than from using BMD (0.58), indicating a better test performance using fractal dimension. Therefore, computerized radiographic methods to evaluate bone structure, such as fractal analysis, may be helpful in better determining fracture risk in osteoporosis.
Adult↗
Computer vision and artificial intelligence in mammography.
The revolution in digital computer technology that has made possible new and sophisticated imaging techniques may next influence the interpretation of radiologic images. In mammography, computer vision and artificial intelligence techniques have been used successfully to detect or to characterize abnormalities on digital images. Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists. This technology therefore could decrease errors in mammographic interpretation that continue to plague human observers.
Artificial Intelligence↗
Simulation studies of data classification by artificial neural networks: potential applications in medical imaging and decision making.
Artificial neural networks are being investigated in the field of medical imaging as a means to facilitate pattern recognition and patient classification. In the work reported here, the effects of internal structure and the nature of input data on the performance of neural networks were investigated systematically using computer-simulated data. Network performance was evaluated quantitatively by means of receiver operating characteristic analysis and compared with the performance of an ideal statistical decision maker. We found that the relatively simple neural networks investigated in this study can perform at the level of an ideal decision maker. These simple networks were also found to learn accurately even when the training data are extremely unbalanced with respect to the prevalence of actually positive cases and to differentiate input data patterns by recognizing their unique characteristics.
Computer Simulation↗