Eigendetection of masses considering false positive reduction and breast density information

Abstract

The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.

A Statistical Approach for Breast Density Segmentation

“…8 Therefore, the segmentation of the breast density might be beneficial not only for estimating the quantity of breast dense tissue but also for establishing independent strategies in fatty or dense regions where an automatic procedure may be used to look for abnormalities. 9 During the last years, different algorithms have been proposed for breast density segmentation. For instance, Boyd et al 10 and Sivaramakrishna et al 11 used a grey-level thresholding technique to segment the breast into dense and fatty regions.…”

A New CAD System for Breast Microcalcifications Diagnosis

“…Another type of single view techniques is models based approaches based on comparing the patient mammograms to known images of healthy and pathological cases [17,30].…”

Simultaneous detection and segmentation for generic objects

“…Using object recognition techniques, possible abnormalities could be automatically detected to help doctors to detect and diagnose. For instance, various approaches have been proposed for a computer aided diagnosis (CAD) in mammographic images [51,65]. En example is illustrated in Figure 1.3b of the automatic detection of masses.…”