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It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the(More)
The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and(More)
The number of women with breast implants is increasing. Radiologists must be familiar with the normal and abnormal findings of common implants. Implant rupture is a well-known complication after surgery and is the main cause of implant removal. Although mammography and ultrasonography are the standard first steps in the diagnostic workup, magnetic resonance(More)
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(More)
Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of(More)
Mammographic risk assessment provides an indication of the likelihood of women developing breast cancer. Anumber ofmammographic image based classification methods have been developed, such as Wolfe, Boyd, BI-RADS and Tabár based assessment. We provide a comparative study of these four approaches. Results on the full MIAS database are presented, which(More)
RATIONALE AND OBJECTIVES The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be(More)
BACKGROUND Human cytomegalovirus (HCMV) has established itself as the most significant cause of congenital infection in the developed world. The objective of this research was prenatal identification of pregnant women at risk for developing active infection due to HCMV as well as to diagnose congenitally infected newborns. METHODS A diagnostic algorithm(More)
This paper presents a comparison of two clustering based algorithms and one region based algorithm for segmenting fatty and dense tissue in mammographic images. This is a crucial step in order to obtain a quantitative measure of the density of the breast. The first algorithm is a multiple thresholding algorithm based on the excess entropy, the second one is(More)