Abstract
Several studies have showed that increased mammographic density is an important risk factor for breast cancer. Dense tissue often appears as textured regions in mammograms, so density and texture estimation are inextricably linked. It has been demonstrated that texture classes can be learned, and that subsequently textures can be classified using the joint distribution of intensity values over extremely compact neighbourhoods. Motivated by the success of texture classification, we propose an fully automated scheme for mammogram texture classification and segmentation. The classification method first has a training step to model the joint distribution for each breast density class. Subsequently, a statistical comparison is used to determine the class label for new images. Inspired by the classification, we combine the so-called image patch method with a HMRF(Hidden Markov Random Field) to achieve mammogram segmentation.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gong, Y.C., Brady, M., Petroudi, S. (2006). Texture Based Mammogram Classification and Segmentation. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_83
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DOI: https://doi.org/10.1007/11783237_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
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