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In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases,(More)
We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an(More)
We present a computer-aided diagnosis (CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid(More)
RATIONALE AND OBJECTIVES To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer. MATERIALS AND METHODS An institutional review board approved protocol was obtained, with waiver of consent for(More)
RATIONALE AND OBJECTIVES To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound. MATERIALS AND METHODS Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method(More)
RATIONALE AND OBJECTIVES Our goal was to investigate the effects of changes that the prevalence of cancer in a population have on the probability of malignancy (PM) output and an optimal combination of a true-positive fraction (TPF) and a false-positive fraction (FPF) of a mammographic and sonographic automatic classifier for the diagnosis of breast cancer.(More)
PURPOSE To evaluate a computer-aided diagnosis multimodality intelligent workstation as an aid to radiologists in the interpretation of mammograms and breast sonograms. MATERIALS AND METHODS An institutional review board approved the protocol for an observer study with signed consent, as well as the retrospective use of the mammograms, sonograms, and(More)
RATIONALE AND OBJECTIVES The purpose of this study is to investigate the use of computer-extracted features of lesions imaged by means of two modalities, mammography and breast ultrasound, in the computerized classification of breast lesions. MATERIAL AND METHODS We performed computerized analysis on a database of 97 patients with a total of 100 lesions(More)
RATIONALE AND OBJECTIVES To investigate the effect of different reporting methods and performance measures on the assessment of the benefit of computer-aided diagnosis (CAD) in characterizing malignant and benign breast lesions on mammography and sonography. MATERIALS AND METHODS In a previous study, 10 observers provided three types of reporting data(More)
RATIONALE AND OBJECTIVES To evaluate variability in the clinical assessment of breast images, we evaluated scoring behavior of radiologists in a retrospective reader study combining x-ray mammography (XRM) and three-dimensional automated breast ultrasound (ABUS) for breast cancer detection in women with dense breasts. METHODS The study involved 17 breast(More)