基于自适应块聚类的医学图像超分辨重建

医学图像在病人的诊疗过程中具有重要的参考意义。然而,受设备分辨率和放射剂量的影响,现有设备获得的医学图像分辨率较低,容易对最终诊疗结果产生不利影响。针对这个问题,提出了一种自适应块聚类的医学图像超分辨重建算法。首先,该算法对图像进行四叉树分解,自适应地获得不同尺度的图像块；然后,通过图像块特征提取和聚类处理得到各个不同尺度图像块的聚类中心；最后,利用聚类中心和相应的回归系数重建出高分辨率图像。实验结果表明,所提方法在医学图像重建效果和峰值信噪比、结构相似性对比等方面能够取得更好的效果。

Medical Image Super Resolution Reconstruction Based on Adaptive Patch Clustering

Medical images,e.g.computed tomography(CT),magnetic resonance imaging(MRI) and positron emission tomography (PET),have important significance during the process of diagnosis and treatment for a lot of diseases.However,influenced by the restriction of equipment resolution and radiation dosage,the low resolution problem of medi-cal images is likely to adversely affect the final diagnosis and treatment.Aiming at this problem,a medical image super-resolution reconstruction algorithm of adaptive patch clustering was proposed.Firstly,a set of image patches in different scales,which is adaptive access to gray consistency,can be obtained by using of the algorithm of quad-tree decomposition for images.Then,the algorithm extracts features of these image patches,and clusters the patches to many centers of different scales after the process of clustering.Finally,the different scale centers will be used to reconstruct a high resolution image according to the clustering centers and the corresponding regression coefficients.The experimental results show that the new method performs better in medical image reconstruction,peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).