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Abstract
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| Mass detection in mammography is a complex and challenge problem for digital image processing. Partitional clustering algorithms are a good alternative for automatic detection of such elements, but have the disadvantage of having to segment an image into a number of regions, the number of which is unknown in advance, in addition to discrete approximations of the regions of interest. In this work we use a method of image sub-segmentation to identify possible masses in mammography. The advantage of this method is that the number of regions to segment the image is a known value so the algorithm is applied only once. Additionally, there is a parameter a that can change between 1 and 0 in a continuous way, offering the possibility of a continuous and more accurate approximation of the region of interest. Finally, since the identification of masses is based on the internal similarity of a group data, this method offers the possibility to identify such objects even from a small number of pixels in digital images. This paper presents an illustrative example using the traditional segmentation of images and the sub-segmentation method, which highlights the potential of the alternative we propose for such problems. | |
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International
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Si |
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Congress
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International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2011. |
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960 |
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Place
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Salamanca, Spain |
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Reviewers
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Si |
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ISBN/ISSN
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1867-5662 |
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10.1007/978-3-642-19644-7_62 |
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Start Date
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06/04/2011 |
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End Date
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08/04/2011 |
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From page
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589 |
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To page
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598 |
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Proc. of the International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2011, Advances in Intelligent and Soft Computing, 2011, Volume 87/2011 |