Abstract
This paper presents a novel method that uses incremental self-organizing map (ISOM) network and wavelet transform together for the segmentation of magnetic resonance (MR), computer tomography (CT) and ultrasound (US) images. In order to show the validity of the proposed scheme, ISOM has been compared with Kohonen’s SOM. Two-dimensional continuous wavelet transform (2D-CWT) is used to form the feature vectors of medical images. According to the selected two feature extraction methods, features are formed by the intensity of the pixel of interest or mean value of intensities at one neighborhood of the pixel at each sub-band. The first feature extraction method is used for MR and CT head images. The second method is used for US prostate image.
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© 2006 Springer-Verlag Berlin Heidelberg
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Dokur, Z., Iscan, Z., Ölmez, T. (2006). Segmentation of Medical Images by Using Wavelet Transform and Incremental Self-Organizing Map. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_76
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DOI: https://doi.org/10.1007/11925231_76
Publisher Name: Springer, Berlin, Heidelberg
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