Improvement of computerized mass detection on mammograms: Fusion of two-view information

Abstract

Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more useful if the false-positive rate is reduced. Current CAD systems generally detect and characterize suspicious abnormal structures in individual mammographic images. Clinical experiences by radiologists indicate that screening with two mammographic views improves the detection accuracy of abnormalities in the breast. It is expected that the fusion of information from different mammographic views will improve the performance of CAD systems. We are developing a two-view matching method that utilizes the geometric locations, and morphological and textural features to correlate objects detected in two different views using a prescreening program. First, a geometrical model is used to predict the search region for an object in a second view from its location in the first view. The distance between the object and the nipple is used to define the search area. After pairing the objects in two views, textural and morphological characteristics of the paired objects are merged and similarity measures are defined. Linear discriminant analysis is then employed to classify each object pair as a true or false mass pair. The resulting object correspondence score is combined with its one-view detection score using a fusion scheme. The fusion information was found to improve the lesion detectability and reduce the number of FPs. In a preliminary study, we used a data set of 169 pairs of cranio-caudal (CC) and mediolateral oblique (MLO) view mammograms. For the detection of malignant masses on current mammograms, the film-based detection sensitivity was found to improve from 62% with a one-view detection scheme to 73% with the new two-view scheme, at a false-positive rate of 1 FP/image. The corresponding cased-based detection sensitivity improved from 77% to 91%.

A New CAD System for Breast Microcalcifications Diagnosis

“…In this case, radiologists compare right and left mammograms to seek for abnormalities in both images [15,55,65].…”

Fusion of Two-View Information: SVD Based Modeling for Computerized Classification of Breast Lesions on Mammograms

“…Several studies have demonstrated that the fusion of information extracted from two views, CC and MLO, allow for the reduction of false positive compared to the use of a single image Paquerault et al (2002) Gupta et al (2006.…”

Bilateral analysis based false positive reduction for computer‐aided mass detection

“…Hadjiiski et al 16 developed an interval change analysis of masses on current and prior mammograms and found that the classification accuracy of masses can be improved significantly in comparison to single image classification. Paquerault et al 17 developed a two-view (CC and MLO views) fusion technique to reduce FPs in mass detection and obtained significant improvement by comparing to their one-view detection system. van Engeland et al 18 recently presented a two-view CAD system by using the features including the difference in the radial distance from the candidate regions to the nipple, the gray scale correlation between both regions, and the mass likelihood of the regions determined by the single view CAD scheme.…”

“…The normal fibroglandular tissue in the breast causes FPs by mimicking masses and causes false negatives (FNs) due to overlapping with lesions. In order to improve the performance of our mass detection system, we are investigating computer-vision methods by incorporating information from two-view mammograms 17 and bilateral mammograms,21 emulating radiologists' mammographic interpretation techniques. In this study, we will discuss our approach to FP reduction by analyzing the symmetry or asymmetry of density patterns between bilateral mammograms.…”