A Texture Feature Extraction Technique Using 2D-DFT and Hamming Distance
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Expert Systems with Applications: An International Journal
International Journal of Applied Mathematics and Computer Science
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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.