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Abstract
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| Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCC) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCC in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCC edge detection by Coordinate Logic Filters (CLF), generation, clustering and labelling of suboptimal features vectors by means of Self Organizing Map (SOM) Neural Network. Like comparison we applied an unsupervised clustering K-means in the stage of labelling of our method. In the labelling stage, we obtain better results with the proposed SOM Neural Network compared with the k-means algorithm. Then, we show that the proposed method can locate MCCs in an efficient way | |
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International
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Si |
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Congress
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IWINAC 2009 |
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960 |
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Place
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Santiago de Compostela |
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Reviewers
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Si |
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ISBN/ISSN
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978-3-642-02266-1 |
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10.1007/978-3-642-02267-8_20 |
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Start Date
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22/06/2009 |
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End Date
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26/06/2009 |
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From page
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179 |
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To page
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187 |
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Bioinspired Applications in Artificial and Natural Computation (IWINAC 039;09). LNCS 5602 |