脑CT图象分形插值处理

由于插值方法常用于提高医学图象的质量或用于弥补在有损压缩中丢失的图象信息，因此图象插值在医学图象处理中具有特殊的地位，虽然如今已提出了许多插值方法，然而传统的插值方法（如线性插值、双线性插值等）在处理图象后，会丢失图象的纹理特征，即产生平滑效应。文献1]通过对自然景物图象的灰度研究，证明了自然景物纹理图象的灰度满足各向同性随机分数布朗场（FBR）模型。在此基础上，为克服插值中易产生的平滑问题，提出了一种将分形插值应用于脑CT图象处理的相关参数计算和插值算法。另外，为评价插值图象的质量，同时还引进模糊数学中模糊度和模糊熵的概念，即用模糊度及模糊商来对插值图象质量进行评判。模拟实验结果表明：这种新方法比现有的同类算法（双线性插值）有更好的性能。

Fractal Interpolation Processing Head CT Images

Image interpolation methods used to improve medical image quality on display device or in the field of lossy image compression wherein some pixels. Therefore, image interpolation methods have occupied a peculiar position in medical image processing and many interpolation methods have been presented. But the image processed by the conventional interpolation methods (such as linear interpolation, double-linear interpolation etc.) have lost the image texture and caused smoothness effect. To avoid these effects, many scholars look for other interpolation methods. Through survey the greylevel of the natural image,paper has proved the greylevel of the natural image meet the isotropic fractional brownian of random(FBR). In this paper, we present processing head CT by fractal interpolation. And we have given the method to compute the correlative parameter. To evaluate the processed image quality, we introduce fuzzy degree and fuzzy entropy from fuzzy mathematics. According to the property of fuzzy degree and fuzzy entropy, the high quality image has less fuzzy degree and fuzzy entropy than the low quality image. Simulation results show that this new method has better performance than the other interpolation methods.