Segmentation of ultrasound images by using an incremental self-organized map

A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4/spl times/4 square blocks. In this study, two neural networks for segmentation are comparatively ex...

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Bibliographic Details
Main Authors: Kurnaz, M.N., Dokur, Z., Olmez, T.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Biomedical imaging
Fast Fourier transforms
Feature extraction
Image segmentation
Multi-layer neural network
Neural networks
Pixel
Ultrasonic imaging
Wavelet transforms
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Description
Summary:A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4/spl times/4 square blocks. In this study, two neural networks for segmentation are comparatively examined: Kohonen map, and incremental self-organized map (ISOM). It is observed that ISOM gives the best classification performance with less number of nodes after a short training time.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2001.1017324