Reyer Zwiggelaar

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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)
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)
We describe methods for detecting linear structures in mammograms, and for classifying them into anatomical types (vessels, spicules, ducts, etc). Several different detection methods are compared, using realistic synthetic images and receiver operating characteristic (ROC) analysis. There are significant differences (p<0.001) between the methods, with the(More)
A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is(More)
Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain(More)
Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. In this paper we concentrate on the detection of spiculated lesions in mammograms. A spiculated lesion is typically characterized by an abnormal pattern of linear structures and a central mass. Statistical models have been developed to describe and(More)
We have developed an approach to the separation of background texture and structures in images. The developed approach is based on the statistical difference between local and median co-occurrence matrices. It is our assertion that the classification of mammographic parenchymal patterns can be improved if anatomical structures can be removed from the image(More)
Mammographic risk assessment is becoming increasingly important in decision making in screening mammography and computer aided diagnosis systems. Strong evidence shows that characteristic patterns of breast tissue as seen in mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative(More)