Color image segmentation based on wavelet transformation and S OFM neural network
Jun Zhang, Qieshi Zhang · 2007
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.