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R M Nishikawa

Publications and source records attributed to R M Nishikawa.

At least 19 recordsLinked to original sources
PubMed2006

Computerized mass detection for digital breast tomosynthesis directly from the projection images.

Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number.of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

Algorithms↗
PubMed2004

Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation.

Initial results for a computerized mass lesion detection scheme for digital breast tomosynthesis (DBT) images are presented. The algorithm uses a radial gradient index feature for the initial lesion detection and for segmentation of lesion candidates. A set of features is extracted for each segmented partition. Performance of two- and three dimensional features was compared. For gradient features, the additional dimension provided no improvement in classification performance. For shape features, classification using 3D features was improved compared to the 2D equivalent features. The preliminary overall performance was 76% sensitivity at 11 false positives per exam, estimated based on DBT image data of 21 masses. A larger database will allow for further development and improvement in our computer aided detection scheme.

Breast Neoplasms↗
PubMed2001

Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications.

Our purpose was to study the dependence of computer performance in classifying clustered microcalcifications as malignant or benign on the correct detection of microcalcifications. Specifically, we studied the effects of computer-detected true-positive microcalcifications and computer-detected false-positive microcalcifications in true microcalcification clusters. Using a database of 100 mammograms, we compared computer classification performance obtained from computer-detected microcalcifications to (1) computer classification performance obtained from manually identified microcalcifications, and (2) radiologists' performance. When an artificial neural network (ANN) was trained with manually identified microcalcifications, computer classification performance was comparable to or better than radiologists' performance as the number of computer-detected true-positive microcalcifications decreased to 40% and as the number of computer-detected false-positive microcalcifications increased to 50%. Further loss in computer-detected true-positive microcalcifications degraded classification performance substantially. Moreover, training the ANN with computer-detected microcalcifications also degraded computer classification performance. These results show that computer performance in classifying clustered microcalcifications as malignant or benign is insensitive to moderate decreases in computer-detected true-positive microcalcifications and moderate increases in computer-detected false-positive microcalcifications.

Breast Neoplasms↗
PubMed2001

Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications.

PURPOSE: To evaluate whether computer-aided diagnosis can reduce interobserver variability in the interpretation of mammograms. MATERIALS AND METHODS: Ten radiologists interpreted mammograms showing clustered microcalcifications in 104 patients. Decisions for biopsy or follow-up were made with and without a computer aid, and these decisions were compared. The computer was used to estimate the likelihood that a microcalcification cluster was due to a malignancy. Variability in the radiologists' recommendations for biopsy versus follow-up was then analyzed. RESULTS: Variation in the radiologists' accuracy, as measured with the SD of the area under the receiver operating characteristic curve, was reduced by 46% with computer aid. Access to the computer aid increased the agreement among all observers from 13% to 32% of the total cases (P <.001), while the kappa value increased from 0.19 to 0.41 (P <.05). Use of computer aid eliminated two-thirds of the substantial disagreements in which two radiologists recommended biopsy and routine screening in the same patient (P <.05). CONCLUSION: In addition to its demonstrated potential to improve diagnostic accuracy, computer-aided diagnosis has the potential to reduce the variability among radiologists in the interpretation of mammograms.

Biopsy↗
PubMed2000

Computer-aided detection and diagnosis of breast cancer.

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Algorithms↗
PubMed2000

Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group.

PURPOSE: To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. MATERIALS AND METHODS: Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. RESULTS: For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA algorithms were not significantly different. All printed digital images were preferred to screen-film radiographs in the diagnosis of masses; mammograms processed with unsharp masking were significantly preferred. For the diagnosis of calcifications, no processed digital mammogram was preferred to screen-film mammograms. CONCLUSION: When digital mammograms were preferred to screen-film mammograms, radiologists selected different digital processing algorithms for each of three mammographic reading tasks and for different lesion types. Soft-copy display will eventually allow radiologists to select among these options more easily.

Algorithms↗
PubMed1999

Computer-aided diagnosis in radiology: potential and pitfalls.

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.

Breast Neoplasms↗
PubMed1999

Improving breast cancer diagnosis with computer-aided diagnosis.

RATIONALE AND OBJECTIVES: The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis. MATERIALS AND METHODS: The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations. RESULTS: The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%). CONCLUSION: CAD can be used to improve radiologists' performance in breast cancer diagnosis.

Area Under Curve↗
PubMed1998

Optimization and FROC analysis of rule-based detection schemes using a multiobjective approach.

Computerized detection schemes have the potential of increasing diagnostic accuracy in medical imaging by alerting radiologists to lesions that they initially overlooked. These schemes typically employ multiple parameters such as threshold values or filter weights to arrive at a detection decision. In order for the system to have high performance, the values of these parameters need to be set optimally. Conventional optimization techniques are designed to optimize a scalar objective function. The task of optimizing the performance of a computerized detection scheme, however, is clearly a multiobjective problem: we wish to simultaneously improve the sensitivity and false-positive rate of the system. In this work we investigate a multiobjective approach to optimizing computerized rule-based detection schemes. In a multiobjective optimization, multiple objectives are simultaneously optimized, with the objective now being a vector-valued function. The multiobjective optimization problem admits a set of solutions, known as the Pareto-optimal set, which are equivalent in the absence of any information regarding the preferences of the objectives. The performances of the Pareto-optimal solutions can be interpreted as operating points on an optimal free-response receiver operating characteristic (FROC) curve, greater than or equal to the points on any possible FROC curve for a given dataset and detection scheme. It is demonstrated that generating FROC curves in this manner eliminates several known problems with conventional FROC curve generation techniques for rule-based detection schemes. We employ the multiobjective approach to optimize a rule-based scheme for clustered microcalcification detection that has been developed in our laboratory.

Algorithms↗
PubMed1998

Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms.

We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.

Biophysical Phenomena↗
PubMed1998

Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms.

Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.

Breast Neoplasms↗
PubMed1998

A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms.

Computer-aided diagnosis (CAD) schemes have the potential of substantially increasing diagnostic accuracy in mammography by providing the advantages of having a second reader. Our laboratory has developed a CAD scheme for detecting clustered microcalcifications in digital mammograms that is being tested clinically at the University of Chicago Hospitals. Our CAD scheme contains a large number of parameters such as filter weights, threshold levels, and region of interest (ROI) sizes. The choice of these parameter values determines the overall performance of the system and thus must be carefully set. Unfortunately, when the number of parameters becomes large, it is very difficult to obtain the optimal performance, especially when the values of the parameters are correlated with each other. In this study, we address the problem of identifying the optimal overall performance by developing an automated method for the determination of the parameter values that maximize the performance of a mammographic CAD scheme. Our method utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures the performance of the scheme. Using a database of 89 digitized mammograms, our method demonstrated that the sensitivity of our CAD scheme can be increased from 80% to 87% at a false positive rate of 1.0 per image. We estimate the average performance of our CAD scheme on unknown cases by performing jackknife tests; this was previously not feasible when the parameters of the CAD scheme were determined in a nonautomated manner.

Algorithms↗
PubMed1998

Mammographic databases.

Large mammographic databases are needed for teaching residents and for developing automated computer analysis of mammograms. A collection of digital mammograms would facilitate uniformity in education and would allow for meaningful comparisons of different computerized techniques. In the future, databases will consist of full-field digital mammograms; currently, digitized mammograms are being used. A number of small databases are available, and several large databases (1,000 or more cases) are being developed. This review describes the requirements of the images comprising the database (e.g., spatial resolution), the requirements of an ideal database (e.g., the types of images to include, size, organization), and the characteristics of the existing and planned databases.

Journal Article↗
PubMed1997

Comparison of eye position versus computer identified microcalcification clusters on mammograms.

The purpose of this study was to compare identifications of microcalcification clusters on mammograms by a computerized detection scheme and by human observers having their eye position recorded. Eighty digitized mammograms (half with a subtle microcalcification cluster) were analyzed by a computerized detection scheme and then were read from laser-printed films by six mammographers while eye position was recorded. The computer had 83% true positives with a false-positive rate of 0.5 per image. The true positives of the radiologists ranged from 78% to 90%, with false-positive rates ranging from 0.03 to 0.20. Locations of true and false positives identified by computer and by the human were compared. All but 5% of the true clusters were identified by either the computer, human, or by both. Here 10% of the clusters were detected by only the computer, and 11% were missed by the computer but detected by at least one radiologist. False positives were of three types: identified by computer only, by the human reader only, or by both. Eye-position data indicated significant differences in dwell time between both true-positive and false-positive locations reported by the radiologist versus the computer detections. A follow-up analysis indicated that microcalcification clusters and false positives were judged to have more identifiable characteristics of true calcifications and were associated with longer gaze durations than those with fewer microcalcification characteristics. In general, the computer was able to detect clusters judged to have few or no features that the radiologists were not able to detect. Comparison of computer versus human identification of microcalcification clusters may be useful for improving computerized detection schemes to serve as clinical aids to mammographers, and for understanding what image features lead to false-positive decisions for both the computer and the human reader.

Breast Diseases↗
PubMed1996

An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms.

RATIONALE AND OBJECTIVES: We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS: A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS: The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION: The wavelet transform approach can improve the detection of subtle clustered microcalcifications.

Calcinosis↗
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↗