A Novel Breast Tissue Density Classification Methodology
نویسندگان
چکیده
منابع مشابه
Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes.
BACKGROUND Mammographic density has been found to be strongly associated with risk of breast cancer. We have assessed a novel method of assessing breast tissue that is fully automated, does not require an observer, and measures the volume, rather than the projected area, of the relevant tissues in digitized screen-film mammogram. METHODS Sixteen mammography machines in seven locations in Toro...
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It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Although different approaches in this area have been proposed in recent years, only a few are based on the BIRADS classification standard. In...
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A recent trend in digital mammography are CAD systems, which are computerized tools designed to help 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 is increased. In addition, the suitability of abnormality segmentation approaches tends to ...
متن کاملClassification of Breast Density in Digital Mammograms
In this paper we investigate a new approach to the classification of mammo graphic images according to breast type based on the underlying texture contained within the breast tissue. Three methods for quantifying the texture are considered and used as input in the evaluation of four different classifiers. In this study we examine two classification tasks, a three-class classification problem be...
متن کاملBreast Density Classification Using Multiple Feature Selection
Mammography as an x-ray method usually gives good results for lower density breasts while higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms knowledge about breast density can be useful for setting an appropriate decision threshold in order to produce more accurate detection. Because the...
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ژورنال
عنوان ژورنال: IEEE Transactions on Information Technology in Biomedicine
سال: 2008
ISSN: 1089-7771
DOI: 10.1109/titb.2007.903514