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C J Vyborny

Publications and source records attributed to C J Vyborny.

At least 19 recordsLinked to original sources
PubMed1998

Automated computerized classification of malignant and benign masses on digitized mammograms.

RATIONALE AND OBJECTIVES: To develop a method for differentiating malignant from benign masses in which a computer automatically extracts lesion features and merges them into an estimated likelihood of malignancy. MATERIALS AND METHODS: Ninety-five mammograms depicting masses in 65 patients were digitized. Various features related to the margin and density of each mass were extracted automatically from the neighborhoods of the computer-identified mass regions. Selected features were merged into an estimated likelihood of malignancy by, using three different automated classifiers. The performance of the three classifiers in distinguishing between benign and malignant masses was evaluated by receiver operating characteristic analysis and compared with the performance of an experienced mammographer and that of five less experienced mammographers. RESULTS: Our computer classification scheme yielded an area under the receiver operating characteristic curve (Az) value of 0.94, which was similar to that for an experienced mammographer (Az = 0.91) and was statistically significantly higher than the average performance of the radiologists with less mammographic experience (Az = 0.81) (P = .013). With the database used, the computer scheme achieved, at 100% sensitivity, a positive predictive value of 83%, which was 12% higher than that for the performance of the experienced mammographer and 21% higher than that for the average performance of the less experienced mammographers (P < .0001). CONCLUSION: Automated computerized classification schemes may be useful in helping radiologists distinguish between benign and malignant masses and thus reducing the number of unnecessary biopsies.

Breast Neoplasms↗
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↗
PubMed1996

Malignant and benign clustered microcalcifications: automated feature analysis and classification.

PURPOSE: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer. MATERIALS AND METHODS: One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications. RESULTS: Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03). CONCLUSION: Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

Breast Diseases↗
PubMed1995

Computer-aided detection of clustered microcalcifications on digital mammograms.

A computer-aided diagnosis scheme to assist radiologists in detecting clustered microcalcifications from mammograms is being developed. Starting with a digital mammogram, the scheme consists of three steps. First, the image is filtered so that the signal-to-noise ratio of microcalcifications is increased by suppression of the normal background structure of the breast. Secondly, potential microcalcifications are extracted from the filtered image with a series of three different techniques: a global thresholding based on the grey-level histogram of the full filtered image, an erosion operator for eliminating very small signals, and a local adaptive grey-level thresholding. Thirdly, some false-positive signals are eliminated by means of a texture analysis technique, and a non-linear clustering algorithm is then used for grouping the remaining signals. With this method, the scheme can detect approximately 85% of true clusters, with an average of two false clusters detected per image.

Breast Diseases↗
PubMed1995

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↗
PubMed1995

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↗
PubMed1994

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↗
PubMed1994

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↗
PubMed1994

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↗
PubMed1994

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↗
PubMed1994

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↗
PubMed1993

Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses.

RATIONALE AND OBJECTIVES: Identification of regions as possible masses on digitized screen film mammograms is an important initial step in the computerized detection of breast carcinomas. Possible masses may be initially extracted using criteria based on optical densities, geometric patterns, and asymmetries between corresponding locations in right and left mammograms. In this study, the usefulness of information arising from mammographic asymmetries for the identification of mass lesions is investigated. METHODS: Two techniques are investigated--a nonlinear bilateral-subtraction technique based on image pairs and a local gray-level thresholding technique based on single images. Detection performances obtained with the two techniques in combination with various feature-analysis techniques are evaluated using 154 pairs of mammograms and compared using free-response receiver operating characteristic (FROC) analysis. RESULTS: The nonlinear bilateral-subtraction technique performed better than the local gray-level thresholding technique. CONCLUSION: The incorporation of asymmetric information appears to be useful for computerized identification of possible masses on mammograms.

Female↗
PubMed1993

An "intelligent" workstation for computer-aided diagnosis.

Computer-aided diagnosis (CAD) involves a computerized analysis of radiographs that is used as a "second opinion" by the radiologist. The approach presented incorporates computer vision and artificial intelligence techniques and includes schemes for the analysis of lung nodules, interstitial infiltrates, and cardiomegaly seen on chest radiographs; masses and clustered microcalcifications on mammograms; and stenoses and blood flow on angiograms. The demonstration of various CAD schemes in chest radiography and mammography on a six-monitor workstation simulates one possible clinical implementation of CAD in radiology. Whether soft- or hard-copy display media are used, the radiologist can refer to the CAD results and still use the original radiograph for the final diagnosis. Although initial impressions of this simulated "intelligent" workstation are encouraging, CAD is still in a preliminary stage of development. Various methods for effectively and efficiently integrating CAD into a clinical radiology department are being investigated.

Angiography↗
PubMed1993

Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.

The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.

Female↗
PubMed1990

Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.

Relatively simple, but important, detection tasks in radiology are nearing accessibility to computer-aided diagnostic (CAD) methods. The authors have studied one such task, the detection of clustered microcalcifications on mammograms, to determine whether CAD can improve radiologists' performance under controlled but generally realistic circumstances. The results of their receiver operating characteristic (ROC) study show that CAD, as implemented by their computer code in its present state of development, does significantly improve radiologists' accuracy in detecting clustered microcalcifications under conditions that simulate the rapid interpretation of screening mammograms. The results suggest also that a reduction in the computer's false-positive rate will further improve radiologists' diagnostic accuracy, although the improvement falls short of statistical significance in this study.

Calcinosis↗
PubMed1989

Occult lung carcinoma presenting with dysphagia. The value of computed tomography.

Dysphagia is a relatively uncommon presenting symptom of lung carcinoma that usually occurs in association with mediastinal adenopathy. However, a bronchogenic carcinoma will occasionally involve the esophagus by direct invasion. These central lesions can be difficult to visualize on chest radiographs and may not be detected by esophagoscopy or barium swallow. In such cases, a computed tomography scan of the thorax may suggest the correct diagnosis.

Adult↗
PubMed1989

Mammography as a radiographic examination: an overview.

The mammographic examination can be considered from many different perspectives, not the least of which include the complex diagnostic or public health issues that determine the place of this study in modern medical practice. There is, however, no finer example than mammography of the role of radiological science in radiography. It is important that radiologists remain ever cognizant of this role in order to maximize the benefit of the examination to their patients.

Breast Neoplasms↗