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Combining pixon concept with wavelet thresholding in medical image segmentation

机译:在医学图像分割中将pixon概念与小波阈值相结合

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This paper presents an innovative pixon-based method for image segmentation. The novel method uses the combination of wavelet thresholding and the pixon concept. In our method the wavelet thresholding technique is used to smooth the image and prepare it for a more efficient pixon forming. In addition, utilizing the wavelet transform results in decreasing the pixons number, a faster performance and more robustness against unwanted environmental noises. In the next stage, the appropriate pixons are extracted and eventually we segment the image with the use of a hierarchical clustering method. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods.
机译:本文提出了一种创新的基于像素的图像分割方法。该新方法结合了小波阈值法和pixon概念。在我们的方法中,小波阈值化技术用于平滑图像并为更有效的象素形成做准备。此外,利用小波变换可减少像素数,更快的性能和更强的抗有害环境噪声的能力。在下一步中,将提取适当的像素,最后使用分层聚类方法对图像进行分割。在几种不同的图像上应用该方法的结果表明,与其他方法相比,该方法在图像分割方面具有更好的性能。

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