PubMed Health⌕ Search

Biomedical subjects

R M Nishikawa

Publications and source records attributed to R M Nishikawa.

At least 19 recordsLinked to original sources
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↗
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↗
PubMed1996

An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms.

A shift-invariant artificial neutral network (SIANN) has been applied to eliminate the false-positive detections reported by a rule-based computer aided-diagnosis (CAD) scheme developed in our laboratory. Regions of interest (ROIs) were selected around the centers of the rule-based CAD detections and analyzed by the SIANN. In our previous study, background-trend correction and pixel-value normalization were used as the preprocessing of the ROIs prior to the SIANN. A ROI is classified as a positive ROI, if the total number of microcalcifications detected in the ROI is greater than a certain number. In this study, modifications were made to improve the performance of the SIANN. First, the preprocessing is removed because the result of the background-trend correction is affected by the size of ROIs. Second, image-feature analysis is employed to the output of the SIANN in an effort to eliminate some of the false detections by the SIANN. In order to train the SIANN to detect microcalcifications and also to extract image features of microcalcifications, the zero-mean-weight constraint and training-free-zone techniques have been developed. A cross-validation training method was also applied to avoid the overtraining problem. The performance of the SIANN was evaluated by means of ROC analysis using a database of 39 mammograms for training and 50 different mammograms for testing. The analysis yielded an average area under the ROC curve (A(z)) of 0.90 for the testing set. Approximately 62% of false-positive clusters detected by the rule-based scheme were eliminated without any loss of the true-positive clusters by using the improved SIANN with image feature analysis techniques.

Biophysical Phenomena↗
PubMed1996

Density correction of peripheral breast tissue on digital mammograms.

When digital mammograms are viewed on video displays, evaluation of the skin and subcutaneous tissue is often difficult and may require special window settings. An algorithm has been developed for selective enhancement (ie, density correction) of the dark peripheral portions of the breast on mammograms. After an automated segmentation of the digital mammogram and identification of the skin line, a fitted enhancement curve is generated to selectively enhance all pixels within a certain distance from the skin to match the density of the center part of the breast. After enhancement, skin and breast parenchyma can be evaluated simultaneously without the need for different window settings. When tested on a set of 400 digitized mammograms, the density correction algorithm significantly (P < .0001) increased the maximum area of breast tissue visualized simultaneously at window width settings of 0.5-2.0 delta OD (optical density). Artifacts interfering with interpretation were observed in less than 1%. The algorithm for correcting the density of peripheral breast tissue substantially facilitates and improves the display of digital mammograms and thus will be a valuable component of an integrated workstation for computer-aided diagnosis in mammography.

Algorithms↗
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↗
PubMed1996

Medical physics.

Explore the source record for details and available documents.

Biophysics↗
PubMed1996

A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

PURPOSE: Area under a receiver operating characteristic (ROC) curve (Az) is widely used as an index of diagnostic performance. However, Az is not a meaningful summary of clinical diagnostic performance when high sensitivity must be maintained clinically. The authors developed a new ROC partial area index, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region. MATERIALS AND METHODS: The mathematical formation of the partial area index was derived from the conventional binormal model. Statistical tests of apparent differences in this index were formulated analogous to that of Az. One common statistical test involving the partial area index was validated by computer simulations under realistic conditions. RESULTS: An example in mammography illustrates a situation in which the partial area index is more meaningful than Az in measuring clinical diagnostic performance. CONCLUSION: The partial area index can be used as a more meaningful alternative to the conventional Az index for highly sensitive diagnostic tests.

ROC Curve↗
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↗