Dulcy E. Wolverton

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.

Computerized classification of benign and malignant masses on digitized mammograms: A study of robustness

RATIONALE AND OBJECTIVES The purpose of this study was to evaluate the robustness of a computerized method developed for the classification of benign and malignant masses with respect to variations in both case mix and film digitization. MATERIALS AND METHODS The classification method included automated segmentation of mass regions, automated feature-extraction, and automated lesion characterization. The method was evaluated independently with a 110-case database consisting of 50 malignant and 60 benign cases. Mammograms were digitized twice with two different digitizers (Konica and Lumisys). Performance of the method in differentiating benign from malignant masses was evaluated with receiver operating characteristic (ROC) analysis. Effects of variations in both case mix and film digitization on performance of the method also were assessed. RESULTS Categorization of lesions as malignant or benign with an artificial neural network (or a hybrid) classifier achieved an area under the ROC curve, Az, value of 0.90 (0.94 for the hybrid) on the previous training database in a round-robin evaluation and Az values of 0.82 (0.81) and 0.81 (0.82) on the independent database for the Konica and Lumisys formats, respectively. These differences, however, were not statistically significant (P > .10). CONCLUSION The computerized method for the classification of lesions on mammograms was robust with respect to variations in case mix and film digitization.

Prospective Testing of a Clinical Mammography Workstation for CAD: Analysis of the First 10,000 Cases

For over ten years, we have been developing automated computerized schemes to assist radiologists in detecting breast cancer from mammograms. These detection schemes have been implemented on an “intelligent” mammography workstation that has been used prospectively on screening mammograms for over three years. The purpose of this study was to analyze the performance of the workstation in comparison to radiologists’ clinical interpretations of the same screening mammograms.

Computer-aided diagnosis: analysis of mammographic parenchymal patterns and classification of masses on digitized mammograms

Identification and enhanced surveillance of women at high risk may lead to earlier detection of breast cancer. A computerized method was developed to identify mammographic parenchymal patterns that are associated with breast cancer risk. In this method, various features were first extracted to characterize mammographic parenchymal patterns. These features were then related to breast cancer risk using two different approaches, one based on BRCAI/BRCA2-mutation carriers and one based on clinical risk assessment models. Useful features were identified using stepwise linear discriminant analysis or stepwise linear regression analysis. Results show that increased mammographic density and coarse mammographic patterns correlate with increased breast cancer risk. To potentially help radiologists improve their diagnostic accuracy in classifying benign and malignant breast masses on mammograms, thus reducing the number of biopsies of benign lesions, a computerized classification scheme was developed. The scheme includes three components: (1) automated segmentation of a mass from its parenchymal background, (2) automated feature extraction, and (3) automated classification yielding an estimated likelihood of malignancy. Results show that the scheme can perform similarly to an expert radiologist and significantly better than average general radiologists in distinguishing between benign and malignant masses.

Computer-Aided Diagnosis of Digital Mammography and Ultrasound Images of Breast Mass Lesions

Although general rules for the differentiation between benign and malignant mammographically-identified breast lesions exist, considerable misclassification of lesions occurs with current imaging and interpretation methods [1]–[6]. The purpose of our study is to develop computerized methods for the analysis of mass lesions on digitized mammograms and on ultrasound images for aiding in the task of distinguishing between malignant and benign lesions. This should lead to (1) an improvement in the classification sensitivity for malignant lesions and (2) an increase in the classification specificity and thus, a reduction in the number of unnecessary biopsies. Higher performance is expected when a combination of features from mammographie and ultrasound images is used as an aid to radiologists in the task of distinguishing between malignant and benign lesions.

Mammographic feature analysis of clustered microcalcifications for classification of breast cancer and benign breast diseases

The authors are developing a computer-aided-diagnosis approach of classifying breast cancer and benign breast disease based on clustered microcalcifications in mammograms. The classification (malignant versus benign) is made by an artificial neural network (ANN) using computer-extracted features of microcalcifications and of clusters as input. The final diagnostic recommendation is made by a radiologist who takes the computer-estimated probability of malignancy into consideration.<<ETX>>

Computerized Analysis of Digitized Mammograms of Women with Low Breast Cancer Risk and of BRCA1/BRCA2 Gene-Mutation Carriers

Women who carry the BRCA1/BRCA2 gene are estimated to have a 20% risk of developing breast cancer by age 40 and an 87% lifetime risk of developing breast cancer, which is 8 times as high as that for the general population. Therefore, earlier surveillance of young women at very high risk has been recommended. In this study, we are developing computerized methods to quantitatively characterize breast parenchyma on digitized mammograms. We evaluated the performance of the computerized method using mammograms from women who are BRCAl/BRCA2-mutation carriers and from low-risk women