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Proceedings Paper

A medical image segmentation methods based on SOM and wavelet transforms
Author(s): Liping Zhang
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Paper Abstract

Image segmentation plays a crucial role in many medical imaging applications and is an important but inherently difficult problem. The paper discuses the method that classify unsupervised image using a Kohonen self-organizing map neural network. This method exits two problems: training time of the network is too long and the classified result and quantity were bigger influenced by the noise of image. Two-dimensional Discrete Wavelet Transforms (DWT) decompose MRI image into the small size and denoise approximation images. Kohonen self-organizing map neural network is trained with approximation image, then trained neural network classify pixels of original image. Training time of the network was notability decrease and the classified quality influenced by the noise of image was notability reduce. The technique presented here has shown a very encouraging level of performance for the problem of segmentation in MRI image of the head.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142905 (14 February 2020); doi: 10.1117/12.2535794
Show Author Affiliations
Liping Zhang, Changjiang Polytechnic (China)

Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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