Assessment of calibration methods for estimating bone mineral densities in trauma patients with quantitative CT: an anthropomorphic phantom study.

RATIONALE AND OBJECTIVES: Osteoporosis may contribute to the increased morbidity and mortality of elderly persons involved in motor vehicle accidents. Such patients commonly undergo whole-body computed tomographic (CT) studies that may be analyzed with quantitative CT. Various quantitative CT calibration techniques were investigated for use with patients who have suffered trauma, who are typically scanned on a backboard. MATERIALS AND METHODS: Lumbar simulator phantoms were used to simulate small and large patients. Vertebral spongiosa inserts with a wide range of bone and fat compositions were placed in the phantoms, and their bone mineral densities (BMDs) were measured by using calibration lines derived from the CT numbers of a calibration standard. Four calibration techniques were tested. In three the lumbar simulator and the calibration standard were scanned simultaneously, with the standard placed beneath the backboard (method 1), on top of the backboard adjacent to the lumbar simulator (method 2), or on top of the abdomen region of the lumbar simulator (method 3). The fourth technique employed a single calibration line derived from a separate scan of the calibration standard beneath the small lumbar simulator without the backboard, with correction for patient body size. RESULTS: The best overall results were obtained with the single calibration line method. The root mean square errors of the BMD values were 2.9-18.4, 2.5-7.5, 2.5-14.9, and 0.3-2.8 mg/cm3 for methods 1, 2, 3, and 4, respectively (ranges represent variations in the errors of the measured BMDs of the inserts due to changes in scanner table height and lumbar simulator phantom size). CONCLUSION: The single calibration line method is an accurate means of measuring BMD in trauma patients.

Computerized image analysis: estimation of breast density on mammograms.

An automated image analysis tool is being developed for the estimation of mammographic breast density. This tool may be useful for risk estimation or for monitoring breast density change in prevention or intervention programs. In this preliminary study, a data set of 4-view mammograms from 65 patients was used to evaluate our approach. Breast density analysis was performed on the digitized mammograms in three stages. First, the breast region was segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique was applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification was used to classify the breast images into four classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold was automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area was then estimated. To evaluate the performance of the algorithm, the computer segmentation results were compared to manual segmentation with interactive thresholding by five radiologists. A "true" percent dense area for each mammogram was obtained by averaging the manually segmented areas of the radiologists. We found that the histograms of 6% (8 CC and 8 MLO views) of the breast regions were misclassified by the computer, resulting in poor segmentation of the dense region. For the images with correct classification, the correlation between the computer-estimated percent dense area and the "truth" was 0.94 and 0.91, respectively, for CC and MLO views, with a mean bias of less than 2%. The mean biases of the five radiologists' visual estimates for the same images ranged from 0.1% to 11%. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility of breast density estimation in comparison with the subjective visual assessment by radiologists.

Development of a protocol for coronal reconstruction of the maxillofacial region from axial helical CT data.

Using a fresh frozen cadaver head, a series of axial helical CT scans were obtained using varying imaging parameters both before and after traumatizing the head. The appearance of reformatted coronal images was optimized for the lowest radiation dose. A protocol for imaging the maxillofacial region was developed that produced diagnostic coronal reconstructed images from the axial helical CT data.

Digital mammography: observer performance study of the effects of pixel size on the characterization of malignant and benign microcalcifications.

RATIONALE AND OBJECTIVES: The authors performed this study to evaluate the effects of pixel size on the characterization of mammographic microcalcifications by radiologists. MATERIALS AND METHODS: Two-view mammograms of 112 microcalcification clusters were digitized with a laser scanner at a pixel size of 35 microm. Images with pixel sizes of 70, 105, and 140 microm were derived from the 35-microm-pixel size images by averaging neighboring pixels. The malignancy or benignity of the microcalcifications had been determined with findings at biopsy or 2-year follow-up. Region-of-interest images containing the microcalcifications were printed with a laser imager. Seven radiologists participated in a receiver operating characteristic (ROC) study to estimate the likelihood of malignancy. The classification accuracy was quantified with the area under the ROC curve (Az). The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz method and the Student paired t test. The variance components were analyzed with a bootstrap method. RESULTS: The higher-resolution images did not result in better classification; the average Az with a pixel size of 35 microm was lower than that with pixel sizes of 70 and 105 microm. The differences in Az between different pixel sizes did not achieve statistical significance. CONCLUSION: Pixel sizes in the range studied do not have a strong effect on radiologists' accuracy in the characterization of microcalcifications. The low specificity of the image features of microcalcifications and the large interobserver and intraobserver variabilities may have prevented small advantages in image resolution from being observed.

Stereomammography: evaluation of depth perception using a virtual 3D cursor.

We are evaluating the usefulness of stereomammography in improving breast cancer diagnosis. One area that we are investigating is whether the improved depth perception associated with stereomammography might be significantly enhanced with the use of a virtual 3D cursor. A study was performed to evaluate the accuracy of absolute depth measurements made in stereomammograms with such a cursor. A biopsy unit was used to produce digital stereo images of a phantom containing 50 low contrast fibrils (0.5 mm diam monofilaments) at depths ranging from 1 to 11 mm, with a minimum spacing of 2 mm. Half of the fibrils were oriented perpendicular (vertical) and half parallel (horizontal) to the stereo shift direction. The depth and orientation of each fibril were randomized, and the horizontal and vertical fibrils crossed, simulating overlapping structures in a breast image. Left and right eye images were generated by shifting the x-ray tube from +2.5 degrees to -2.5 degrees relative to the image receptor. Three observers viewed these images on a computer display with stereo glasses and adjusted the position of a cross-shaped virtual cursor to best match the perceived location of each fibril. The x, y, and z positions of the cursor were indicated on the display. The z (depth) coordinate was separately calibrated using known positions of fibrils in the phantom. The observers analyzed images of two configurations of the phantom. Thus, each observer made 50 vertical filament depth measurements and 50 horizontal filament depth measurements. These measurements were compared with the true depths. The correlation coefficients between the measured and true depths of the vertically oriented fibrils for the three observers were 0.99, 0.97, and 0.89 with standard errors of the estimates of 0.39 mm, 0.83 mm, and 1.33 mm, respectively. Corresponding values for the horizontally oriented fibrils were 0.91, 0.28, and 0.08, and 1.87 mm, 4.19 mm, and 3.13 mm. All observers could estimate the absolute depths of vertically oriented objects fairly accurately in digital stereomammograms; however, only one observer was able to accurately estimate the depths of horizontally oriented objects. This may relate to different aptitudes for stereoscopic visualization. The orientations of most objects in actual mammograms are combinations of horizontal and vertical. Further studies are planned to evaluate absolute depth measurements of fibrils oriented at various intermediate angles and of objects of different shapes. The effects of the shape and contrast of the virtual cursor and the stereo shift angle on the accuracy of the depth measurements will also be investigated.

Design and evaluation of an external filter technique for exposure equalization in mammography.

We are developing an external filter method for equalizing x-ray exposure in the peripheral region of the breast. This method requires the use of only a limited number of custom-built filters for different breast shapes in a given view. This paper describes the design methodology for these external filters. The filter effectiveness was evaluated through a simulation study on 171 mediolateral and 196 craniocaudal view digitized mammograms and through imaging of a breast phantom. The degree of match between the simulated filter and the individual 3-D exposure profiles at the breast periphery was quantified. An analysis was performed to investigate the effect of filter misalignment. The simulation study indicates that the filter is effective in equalizing exposures for more than 80% of the breast images in our database. The tolerance in filter misalignment was estimated to be about +/- 2 mm for the CC view and +/- 1 mm for the MLO view at the image plane. Some misalignment artifacts were demonstrated with simulated filtered mammograms.

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.

We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.

Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis.

A genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of 0.1 mm x 0.1 mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection (LDAsfs). With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of LDAsfs, although the latter provided a higher total area (Az) under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and LDAsfs correctly identified 61% and 34% of the benign masses respectively without missing any malignant masses. Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization.

Classification of compressed breast shapes for the design of equalization filters in x-ray mammography.

We are developing an external filter method for equalizing the x-ray exposure in mammography. Each filter is specially designed to match the shape of the compressed breast border and to preferentially attenuate the x-ray beam in the peripheral region of the breast. To be practical, this method should require the use of only a limited number of custom built filters. It is hypothesized that this would be possible if compressed breasts can be classified into a finite number of shapes. A study was performed to determine the number of shapes. Based on the parabolic appearance of the outer borders of compressed breasts in mammograms, the borders were fit with the polynomial equations y = ax2 + bx3 and y = ax2 + bx3 + cx4. The goodness-of-fit of these equations was compared. The a,b and a,b,c coefficients were employed in a K-Means clustering procedure to classify 470 CC-view and 484 MLO-view borders into 2-10 clusters. The mean coefficients of the borders within a given cluster defined the "filter" shape, and the individual borders were translated and rotated to best match that filter shape. The average rms differences between the individual borders and the "filter" were computed as were the standard deviations of those differences. The optimally shifted and rotated borders were refit with the above polynomial equations, and plotted for visual evaluation of clustering success. Both polynomial fits were adequate with rms errors of about 2 mm for the 2-coefficient equation, and about 1 mm for the 3-coefficient equation. Although the fits to the original borders were superior for the 3-coefficient equation, the matches to the "filter" borders determined by clustering were not significantly improved. A variety of modified clustering methods were developed and utilized, but none produced major improvements in clustering. Results indicate that 3 or 4 filter shapes may be adequate for each mammographic projection (CC- and MLO-view). To account for the wide variations in exposures observed at the peripheral regions of breasts classified to be of a particular shape, it may be necessary to employ different filters for thin, medium and thick breasts. Even with this added requirement, it should be possible to use a small number of filters as desired.

CT attenuation of fluid in breast cysts.

RATIONALE AND OBJECTIVES: The authors' purpose was to determine whether computed tomographic (CT) attenuation values of fluid in breast cysts could be in the range of values for soft tissue and could be correlated with protein content of the fluid. MATERIALS AND METHODS: Aspirate samples from 10 simple breast cysts were analyzed for protein content, and CT attenuation values were calculated by means of a breast phantom. A corrected attenuation value for breast-cyst fluid was calculated by using sterile water as a control. RESULTS: The mean corrected attenuation value for the cyst aspirate was 28.1 HU; most simple cysts have an attenuation value of only 10 HU. Protein concentration ranged from 0.9 to 2.4 g/dL. A significant, almost linear relationship was noted between protein content and attenuation value of cyst fluid (r = .85, P < .01). CONCLUSION: The CT attenuation values of breast cysts can be in the range of those of soft tissue. This high attenuation value is correlated with the high protein content of breast-cyst fluid. Therefore, an apparent circumscribed soft-tissue mass seen within the breast at CT may represent a simple cyst.

Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis.

A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented mass onto the Cartesian plane (the RBST image). The border of a mammographic mass appears approximately as a horizontal line, and possible speculations resemble vertical lines in the RBST image. In this study, the effectiveness of a set of directional textures extracted from the images before the RBST. A database of 168 mammograms containing biopsy-proven malignant and benign breast masses was digitized at a pixel size of 100 microns x 100 microns. Regions of interest (ROIs) containing the biopsied mass were extracted from each mammogram by an experienced radiologist. A clustering algorithm was employed for automated segmentation of each ROI into a mass object and background tissue. Texture features extracted from spatial gray-level dependence matrices and run-length statistics matrices were evaluated for three different regions and representations: (i) the entire ROI; (ii) a band of pixels surrounding the segmented mass object in the ROI; and (iii) the RBST image. Linear discriminant analysis was used for classification, and receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy. Using the ROC curves as the performance measure, features extracted from the RBST images were found to be significantly more effective than those extracted from the original images. Features extracted from the RBST images yielded an area (Az) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.

False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.

We investigated the application of multiresolution global and local texture features to reduce false-positive detection in a computerized mass detection program. One hundred and sixty-eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density-Weighted Contrast Enhancement (DWCE) algorithm as false-positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low-pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, Az, under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.

Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.

We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.

Investigation of the line-pair pattern method for evaluating mammographic focal spot performance.

The latest American College of Radiology (ACR) Mammography Quality Control Manual contains a new method for evaluating focal spot performance, which this paper refers to as the "line-pair pattern test." The ACR describes a variety of methods for performing this test, and does not advocate one method over another. The authors of this paper conducted an investigation to compare the optional ways for performing the test. Resolution measurements were obtained using a prototype line-pair resolution phantom imaged with a GE DMR mammography unit. Measurements were made with the line-pair pattern 4.5 cm above the breast support platforms in both conventional (contact) and magnification geometries. Both 4.5 cm of air and Lucite were tested as attenuators between the line-pair pattern and the breast support platform. Image receptors that were employed included film alone, screen-film, and screen-film that was not allowed to wait the recommended 15 min before exposure. kVp was varied as was the orientation of the line-pair pattern relative to the chest wall. For the air attenuator case, the screen degraded the measured resolution by 1-3 lp/mm when compared to the direct film. The Lucite attenuator reduced the resolution by an additional 1 1p/mm. Increasing kVp improved the resolution slightly for the conventional mode, but decreased it slightly for the magnification mode. Based upon the results of this study, recommendations are made for improving the test protocol. For a test of focal spot performance, one should use the no-attenuation with direct film detector setup. For a measure of the resolution of the entire imaging chain, one should use the Lucite attenuator with screen-film detector setup.

Reduction of radiation dose in pediatric patients using pulsed fluoroscopy.

OBJECTIVE: The purpose of this study was to determine if pulsed fluoroscopy reduces radiation exposure to pediatric patients undergoing conventional fluoroscopy. SUBJECTS AND METHODS: Four hundred one consecutive patients were nonrandomly divided into pulsed fluoroscopy and conventional fluoroscopy study groups. Two control groups were also assembled: 474 patients evaluated with conventional fluoroscopy before the study and 138 patients evaluated with pulsed fluoroscopy after the study. RESULTS: We found no difference in fluoroscopy times across the groups. Although the number of digital spot films was slightly higher for the pulsed fluoroscopy study group than for the conventional fluoroscopy study group, we found no difference in the number of digital spot films for the pulsed fluoroscopy study group and for the conventional fluoroscopy control group. Furthermore, the difference in the number of digital spot films was also insignificant for the pulsed fluoroscopy control group and the conventional fluoroscopy study group. The radiation exposure in the pulsed fluoroscopy study group was 50% lower (mean, 0.6 R) than in the conventional fluoroscopy study group. When using pulsed fluoroscopy in the 7.5 pulses-per-second mode, we were able to reduce radiation exposure by 75% of that from conventional fluoroscopy. CONCLUSION: Pulsed fluoroscopy reduces fluoroscopic radiation exposure to pediatric patients undergoing conventional fluoroscopy. Despite minor image degradation, pulsed fluoroscopy is the technique of choice at our institution.

Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue.

We investigated a new approach to feature selection, and demonstrated its application in the task of differentiating regions of interest (ROIs) on mammograms as either mass or normal tissue. The classifier included a genetic algorithm (GA) for image feature selection, and a linear discriminant classifier or a backpropagation neural network (BPN) for formulation of the classifier outputs. The GA-based feature selection was guided by higher probabilities of survival for fitter combinations of features, where the fitness measure was the area Az under the receiver operating characteristic (ROC) curve. We studied the effect of different GA parameters on classification accuracy, and compared the results to those obtained with stepwise feature selection. The data set used in this study consisted of 168 ROIs containing biopsy-proven masses and 504 ROIs containing normal tissue. From each ROI, a total of 587 features were extracted, of which 572 were texture features and 15 were morphological features. The GA was trained and tested with several different partitionings of the ROIs into training and testing sets. With the best combination of the GA parameters, the average test Az value using a linear discriminant classifier reached 0.90, as compared to 0.89 for stepwise feature selection. Test Az values with a BPN classifier and a more limited feature pool were 0.90 with GA-based feature selection, and 0.89 for stepwise feature selection. The use of a GA in tailoring classifiers with specific design characteristics was also discussed. This study indicates that a GA can provide versatility in the design of linear or nonlinear classifiers without a trade-off in the effectiveness of the selected features.

Delayed enhanced CT for differentiation of benign from malignant adrenal masses.

PURPOSE: To determine whether adenomas can be differentiated from nonadenomas on 1-hour-delayed enhanced computed tomographic (CT) scans. MATERIALS AND METHODS: In a prospective evaluation of 51 adrenal masses in 39 patients, the CT attenuation was measured at the time of contrast enhancement and 1 hour later. The results were compared for adenomas (n = 41) and metastases (n = 10). RESULTS: On 1-hour-delayed enhanced CT scans, the mean attenuation of the adenomas was 11 HU +/- 13 versus 49 HU +/- 8.3 for metastases (P < .001). At a threshold value of 30 HU, specificity and positive predictive value for the diagnosis of adenoma were 100% with a sensitivity of 95%. The mean decrease in attenuation during the 1-hour delay was 74% +/- 37 for the adenomas versus 31% +/- 28 for the metastases (P < .001). CONCLUSION: CT densitometry on delayed scans obtained 1 hour after contrast enhancement may be useful in characterizing an adrenal mass as an adenoma. When CT is performed with a 150-mL bolus injection of contrast material and with the scanning parameters described in this study, other procedures or imaging studies may be unnecessary if the mass measures less than 30 HU on the delayed scans.

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images.

The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.

Normalized average glandular dose in magnification mammography.

PURPOSE: To evaluate the normalized average glandular dose (the average glandular dose per unit entrance skin exposure) in magnification mammography. MATERIALS AND METHODS: Photon transport in the breast was simulated by using Monte Carlo methods. A semielliptical cylinder containing glandular and adipose tissue was used to simulate the breast. Measured mammography spectra for a molybdenum target-molybdenum filter unit were utilized. The normalized average glandular dose was calculated as a function of half-value layer, tube voltage, breast thickness, and breast composition for typical magnification geometries. RESULTS: The normalized average glandular dose in magnification mammography is 7%-25% lower than that with the contact (nonmagnification) technique because of the effects of partial irradiation, smaller field size, and greater percentage depth dose gradient at the reduced source-to-skin distance. CONCLUSION: The normalized average glandular dose in magnification mammography is lower than that in contact mammography. The average glandular dose in magnification mammography, however, is still substantially greater due to the two to three times greater entrance skin exposure.