Biomedical subjects

Walter F Good

Publications and source records attributed to Walter F Good.

7 recordsLinked to original sources

PubMed2005

Stereo CT image compositing methods for lung nodule detection and characterization.

RATIONALE AND OBJECTIVES: Stereographic display has been proposed as a possible method of improving performance in reading computed tomographic (CT) examinations acquired for lung cancer screening. Optimizing such displays is important given the large volume of image data that must be evaluated for each of these examinations. This study is designed to explore certain tradeoffs between rendering methods designed for the stereo display of CT images. MATERIALS AND METHODS: Stereo CT image compositing methods, including distance-weighted averaging, distance-weighted maximum intensity projection (MIP), and conventional MIP, were applied to lung CT images and compared for lung nodule detection and characterization. RESULTS: Using the Jonckheere test indicated a statistically significant (P < .01) increase in contrast among the three compositing methods. Wilcoxon-Mann-Whitney test showed significant differences in contrast between distance-weighted averaging and conventional MIP (P < .01) and between averaging and distance-weighted MIP (P < .05), but not between distance-weighted MIP and conventional MIP (P > .05). Conventional MIP compositing provided the highest image contrast, but produced ambiguities in local geometric detail and texture, whereas averaging resulted in the lowest contrast, but preserved geometric detail. Distance-weighted MIP partially recovered geometric information, which was lost in images composited by means of conventional MIP. CONCLUSION: Our results indicate that distance-weighted MIP may be a better choice for nodule detection in stereo lung CT images for its high local contrast and partial preservation of geometric information, whereas compositing by means of distance-weighted averaging is preferable for nodule characterization. The relative clinical value of these compositing methods needs to be evaluated further.

Algorithms↗

PubMed2004

A method to test the reproducibility and to improve performance of computer-aided detection schemes for digitized mammograms.

The purpose of this study is to develop a new method for assessment of the reproducibility of computer-aided detection (CAD) schemes for digitized mammograms and to evaluate the possibility of using the implemented approach for improving CAD performance. Two thousand digitized mammograms (representing 500 cases) with 300 depicted verified masses were selected in the study. Series of images were generated for each digitized image by resampling after a series of slight image rotations. A CAD scheme developed in our laboratory was applied to all images to detect suspicious mass regions. We evaluated the reproducibility of the scheme using the detection sensitivity and false-positive rates for the original and resampled images. We also explored the possibility of improving CAD performance using three methods of combining results from the original and resampled images, including simple grouping, averaging output scores, and averaging output scores after grouping. The CAD scheme generated a detection score (from 0 to 1) for each identified suspicious region. A region with a detection score >0.5 was considered as positive. The CAD scheme detected 238 masses (79.3% case-based sensitivity) and identified 1093 false-positive regions (average 0.55 per image) in the original image dataset. In eleven repeated tests using original and ten sets of rotated and resampled images, the scheme detected a maximum of 271 masses and identified as many as 2359 false-positive regions. Two hundred and eighteen masses (80.4%) and 618 false-positive regions (26.2%) were detected in all 11 sets of images. Combining detection results improved reproducibility and the overall CAD performance. In the range of an average false-positive detection rate between 0.5 and 1 per image, the sensitivity of the scheme could be increased approximately 5% after averaging the scores of the regions detected in at least four images. At low false-positive rate (e.g., < or =average 0.3 per image), the grouping method alone could increase CAD sensitivity by 7%. The study demonstrated that reproducibility of a CAD scheme can be tested using a set of slightly rotated and resampled images. Because the reproducibility of true-positive detections is generally higher than that of false-positive detections, combining detection results generated from subsets of rotated and resampled images could improve both reproducibility and overall performance of CAD schemes.

Algorithms↗

PubMed2004

Computer-aided detection schemes: the effect of limiting the number of cued regions in each case.

OBJECTIVE: We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case. MATERIALS AND METHODS: A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one. RESULTS: The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%. CONCLUSION: Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography.

Breast Neoplasms↗

PubMed2003

Performance change of mammographic CAD schemes optimized with most-recent and prior image databases.

RATIONALE AND OBJECTIVES: The authors evaluated performance changes in the detection of masses on "current" (latest) and "prior" images by computer-aided diagnosis (CAD) schemes that had been optimized with databases of current and prior mammograms. MATERIALS AND METHODS: The authors selected 260 pairs of matched consecutive mammograms. Each current image depicted one or two verified masses. All prior images had been interpreted originally as negative or probably benign. A CAD scheme initially detected 261 mass regions and 465 false-positive regions on the current images, and 252 corresponding mass regions (early signs) and 471 false-positive regions on prior images. These regions were divided into two training and two testing databases. The current and prior training databases were used to optimize two CAD schemes with a genetic algorithm. These schemes were evaluated with two independent testing databases. RESULTS: The scheme optimized with current images produced areas under the receiver operating characteristic curve of (0.89 +/- 0.01 and 0.65 +/- 0.02 when tested with current images and prior images, respectively. The scheme optimized with prior images produced areas under the receiver operating characteristic curve of 0.81 +/- 0.02 and 0.71 +/- 0.02 when tested with current images and prior images, respectively. Performance changes for both current and prior testing databases were significant (P < .01) for the two schemes. CONCLUSION: CAD schemes trained with current images do not perform optimally in detecting masses depicted on prior images. To optimize CAD schemes for early detection, it may be important to include in the training database a large fraction of prior images originally reported as negative and later proven to be positive.

Algorithms↗

PubMed2003

Integrated density of a lesion: a quantitative, mammographically derived, invariable measure.

A method for quantitatively estimating lesion "size" from mammographic images was developed and evaluated. The main idea behind the measure, termed "integrated density" (ID), is that the total x-ray attenuation attributable to an object is theoretically invariant with respect to the projected view and object deformation. Because it is possible to estimate x-ray attenuation of a lesion from relative film densities, after appropriate corrections for background, the invariant property of the measure is expected to result in an objective method for evaluating the "sizes" of breast lesions. ID was calculated as the integral of the estimated image density attributable to a lesion, relative to surrounding background, over the area of the lesion and after corrections for the nonlinearity of the film characteristic curve. This effectively provides a measure proportional to lesion volume. We computed ID and more traditional measures of size (such as "mass diameter" and "effective size") for 100 pairs of ipsilateral mammographic views, each containing a lesion that was relatively visible in both views. The correlation between values calculated for each measure from corresponding pairs of ipsilateral views were computed and compared. All three size-related measures (mass diameter, effective size, and ID) exhibited reasonable linear relationship between paired views (r2>0.7, P<0.001). Specifically, the ID measures for the 100 masses were found to be highly correlated (r2=0.9, P<0.001) between ipsilateral views of the same mass. The correlation increased substantially (r2=0.95), when a measure with linear dimensions of length was defined as the cube root of ID. There is a high degree of correlation between ID-based measures obtained from different views of the same mass. ID-based measures showed a higher degree of invariance than mass diameter or effective size.

Absorptiometry, Photon↗

PubMed2003

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography.

OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.

Aged↗

PubMed2002

Computerized assessment of tissue composition on digitized mammograms.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.

Breast↗