Color image segmentation based on wavelet transformation and SOFM neural network

Zhang Jun, Zhang Qieshi

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Image segmentation, which is the first essential and fundamental issue in the image analysis and pattern recognition, is a classical difficult problem in the image processing. The color images, which possess more visual information than the gray images do, have aroused more and more attentions. In the medical imaging system, according to the different absorbency of different tissues, the staining method is often used to get the color image which provides more abundant information for diagnosis. As for the automatic analysis system of kidney-tissue image stained by Periodic Acid Schiff (PAS), the correct segmentation of glomerulus is an important step. A layer-color clustering segmentation method based on wavelet transformation and self-organizing feature map neural network (SOFM) is proposed in this paper. Firstly, the wavelet transformation is applied to the original images to get the low frequency images to improve the running efficiency. Secondly, the disordered method based on random number is performed to improve the performance of SOFM. Thirdly, the layer-color clustering using SOFM is executed until the final error can meet the need of the average color error (ACE) and then the clustered image and the palette can be acquired. Finally, based on the histogram of palette, the glomerulus can be segmented from the kidney-tissue image correctly. Experimental results show the good performance of this method.

本文言語English
ホスト出版物のタイトル2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
ページ1778-1781
ページ数4
DOI
出版ステータスPublished - 2008
イベント2007 IEEE International Conference on Robotics and Biomimetics, ROBIO - Yalong Bay, Sanya
継続期間: 2007 12 152007 12 18

Other

Other2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
CityYalong Bay, Sanya
Period07/12/1507/12/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Biomaterials

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