Robert A. Schmidt

Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images

A computerized scheme is being developed for the detection of masses in digital mammograms. Based on the deviation from the normal architectural symmetry of the right and left breasts, a bilateral subtraction technique is used to enhance the conspicuity of possible masses. The scheme employs two pairs of conventional screen-film mammograms (the right and left mediolateral oblique views and craniocaudal views), which are digitized by a TV camera/Gould digitizer. The right and left breast images in each pair are aligned manually during digitization. A nonlinear bilateral subtraction technique that involves linking multiple subtracted images has been investigated and compared to a simple linear subtraction method. Various feature-extraction techniques are used to reduce false-positive detections resulting from the bilateral subtraction. The scheme has been evaluated using 46 pairs of clinical mammograms and was found to yield a 95% true-positive rate at an average of three false-positive detections per image. This preliminary study indicates that the scheme is potentially useful as an aid to radiologists in the interpretation of screening mammograms.

Analysis of spiculation in the computerized classification of mammographic masses

Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologists ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.

Automated computerized classification of malignant and benign masses on digitized mammograms

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

Effect of case selection on the performance of computer‐aided detection schemes

The choice of clinical cases used to train and test a computer-aided diagnosis (CAD) scheme can affect the test results (i.e., error rate). In this study, we deliberately modified the components of our testing database to study the effects of this modification on measured performance. Using a computerized scheme for the automated detection of breast masses from mammograms, it was found that the sensitivity of the scheme ranged between 26% and 100% (at a false positive rate of 1.0 per image) depending on the cases used to test the scheme. Even a 20% change in the cases comprising the database can reduce the measured sensitivity by 15%-25%. Because of the strong dependence of measured performance on the testing database, it is difficult to estimate reliably the accuracy of a CAD scheme. Furthermore, it is questionable to compare different CAD schemes when different cases are used for testing. Sharing databases, creating a common database, or using a quantitative measure to characterize databases are possible solutions to this problem. However, none of these solutions exists or is practiced at present. Therefore, as a short-term solution, it is recommended that the method used for selecting cases, and histograms or mean and standard deviations of relevant image features be reported whenever performance data are presented.

COMPUTERIZED DETECTION OF MASSES IN DIGITAL MAMMOGRAMS : AUTOMATED ALIGNMENT OF BREAST IMAGES AND ITS EFFECT ON BILATERAL-SUBTRACTION TECHNIQUE

An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. In this technique, the breast region is first identified in each digital mammogram by use of histogram analysis and morphological filtering operations. The anterior portions of the tracked breast border and computer-identified nipple positions are selected as landmarks for use in image registration. The paired right and left breast images, either from mediolateral oblique or craniocaudal views, are then registered relative to each other by use of a least-squares matching method. This automated alignment technique has been applied to our computerized detection scheme that employs a nonlinear bilateral-subtraction method for the initial identification of possible masses. The effectiveness of using bilateral subtraction in identifying asymmetries between corresponding right and left breast images is examined by comparing detection performances obtained with various computer-simulated misalignments of 40 pairs of clinical mammograms. Based on free-response receiver operating characteristic and regression analyses, the detection performance obtained with the automated alignment technique was found to be higher than that obtained with simulated misalignments. Detection performance decreased gradually as the amount of simulated misalignment increased. These results indicate that automatic alignment of breast images is possible and that mass-detection performance appears to improve with the inclusion of asymmetric anatomic information but is not sensitive to slight misalignment.

Axillary Lymph Nodes Suspicious for Breast Cancer Metastasis: Sampling with US-guided 14-Gauge Core-Needle Biopsy—Clinical Experience in 100 Patients

PURPOSE To study the clinical usefulness of ultrasonography (US)-guided core-needle biopsy (CNB) of axillary lymph nodes and the US-depicted abnormalities that may be used to predict nodal metastases. MATERIALS AND METHODS This retrospective study was HIPAA compliant and institutional review board approved; the requirement for informed patient consent was waived. US-guided 14-gauge CNB of abnormal axillary lymph nodes was performed in 100 of 144 patients with primary breast cancer who underwent US assessment of axillary lymph nodes. A biopsy needle with controllable action rather than a traditional throw-type needle was used. US findings were considered suspicious for metastasis if cortical thickening and/or nonhilar blood flow (NHBF) to the lymph node cortex was present. The absence of any discernible fatty hilum was also noted. RESULTS Nodal metastases were documented at CNB in 64 (64%) of the 100 patients. All 36 patients with negative biopsy results underwent subsequent sentinel lymph node biopsy (SLNB), which yielded negative findings in 32 (89%) patients and revealed metastasis in four (11%). All 44 patients who did not undergo CNB because of negative US results subsequently underwent SLNB, which revealed lymph node metastasis in 12 (27%) patients. Cortical thickening was found in 63 (79%) of the total of 80 metastatic nodes, but only a minority (n = 26 [32%]) of the nodes had an absent fatty hilum. NHBF to the cortex was detected in 52 (65%) metastatic nodes. Both absence of a fatty hilum (metastasis detected in 26 [93%] of 28 nodes) and cortical thickening combined with NHBF (metastasis detected in 52 [81%] of 64 nodes) had a high positive predictive value. No clinically important complications were encountered with the biopsy procedures. CONCLUSION Axillary lymph nodes with abnormal US findings can be sampled with high accuracy and without major complications by using a modified 14-gauge CNB technique.

Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.

A computer-aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme. The shift-invariant neural network is a multilayer back-propagation neural network with local, shift-invariant interconnections. The advantage of the shift-invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift-invariant neural network was evaluated by means of a jackknife (or holdout) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve (Az) of 0.91. Approximately 55% of false-positive ROIs were eliminated without any loss of the true-positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three-layer, feed-forward neural network. The effect of the network structure on the performance of the shift-invariant neural network is also studied.

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.

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

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

ULTRASOUND AS A COMPLEMENT TO MAMMOGRAPHY AND BREAST EXAMINATION TO CHARACTERIZE BREAST MASSES

This study was designed to determine if complementary ultrasound (US) imaging and Doppler could decrease the number of biopsies for benign masses. A total of 761 breast masses were sequentially scored on a level of suspicion (LOS) of 1-5, where 1 represented low, and 5 was a high suspicion of malignancy, for mammography, US, and color flow with pulse Doppler (DUS). After biopsy, the results were analyzed using 2 x 2 contingency tables and ROC analysis, for mammography alone and in combination with US and DUS. The addition of US increased the specificity from 51.4% to 66.4% at a prevalence of 31.3% malignancy. ROC analysis showed that the addition of US significantly improved the performance over mammography alone in women < 55 years old (p = 0.049); > 55 years old (p = 0.029); masses < 1 cm (p = 0.016) and masses > 1 cm (p = 0.016). These results show that the addition of US to mammography alone could substantially reduce the number of breast biopsies for benign disease.