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IEEE Trans. Med. Imag. 20(8), 792–803 (2001) 9. : Optimal segmentation of mammographic images. In: Int. Work. Dig. Mammography, pp. 751–757 (2004) 10. : Breast density segmentation using texture. , Zwiggelaar, R. ) IWDM 2006. LNCS, vol. 4046, pp. 609–615. Springer, Heidelberg (2006) 11. : Structural information in two-dimensional patterns: Entropy convergence and excess entropy (2002) 12. : Elements of Information Theory. Wiley Series in Telecommunications (1991) 13. : High-dimensional normalized mutual information for image registration using random lines.

Thus, a tradeoff between the accuracy of the measure and the number of clusters has to be assumed. To overcome the neighborhood problem, the uniformly distributed random lines approach is used [13]. Points chosen on each line provide us with the intensities to calculate the L-block entropies, required to compute the excess entropy Breast Density Segmentation 11 Initial Offset line intersecting the 3D model Regular Step 1010 10 11 00 L Fig. 1. Uniformly distributed random lines are cast from the walls of the bounding box.

Biomed. E. uk 2 Department of Informatics, Universitas Islam Indonesia, Jl. 4 Sleman 55584, Indonesia 3 Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK Abstract. e. nodular, linear, homogeneous, and radiolucent) as described by Tab´ ar’s tissue model. Such segmentation results are expected to lead to improvements in automatic mammographic risk assessment modelling. The texton selection process has three distinct components, covering a) texton ranking, b) outlier detection, and c) visual assessment.

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Analytic Number Theory: Proceedings of the Japanese-French Symposium Held in Tokyo by Dold A. (Ed), Eckmann B. (Ed), Takens F. (Ed)

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