Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network
نویسندگان
چکیده
منابع مشابه
Background Texture Extraction for the Classification of Mammographic Parenchymal Patterns
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 and the classification is based only o...
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Breast parenchymal density has been found to be a strong indicator for breast cancer risk [1], however, to date, measures of breast density are qualitative and require the judgement of the radiologist. Objective, quantitative measures of breast density are crucial tools for assessing the association between the risk of breast cancer and mammographic density as well as for quantification of dens...
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RATIONALE AND OBJECTIVES Our project was to investigate a complete methodology for the semiautomatic assessment of digital mammograms according to their density, an indicator known to be correlated to breast cancer risk. The BI-RADS four-grade density scale is usually employed by radiologists for reporting breast density, but it allows for a certain degree of subjective input, and an objective ...
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This paper presents a novel selforganizing type RBF neural network and introduces the geometric algebra in the neural computing eld. Real valued neural nets for function approximation require feature enhancement, dilation and rotation operations and are limited by the Euclidean metric. This coordinate-free geometric framework allows to process patterns between layers in a particular dimension a...
متن کاملMammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments
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 evaluation of the characteristic mixture o...
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ژورنال
عنوان ژورنال: Journal of Biomedicine and Biotechnology
سال: 2008
ISSN: 1110-7243,1110-7251
DOI: 10.1155/2008/526343