Biased anisotropic diffusion method for PET image segmentation

In the context of functional positron emission tomographic (PET) images analysis, the segmentation method can not only entails the separation of the image into regions of similar attribute but also presents clearer understanding about the features embedded in the original image to improve the quantitative analysis. However, for completely recording, clinical instruments often collect subject signal as well as signals from background environment, which are regarded as noises of various levels. High noise often makes the original PET image unrecognizable and difficult to analyze. Thus, manual or semiautomatic methods have been utilized to overcome the difficulty of high noise image segmentation. Furthermore, the success of image segmentation is one of the important key factors in the accompanying automated system, and there has been no general segmentation method that can be applied to the high noise PET images of different feature characteristics. However, the PET image is high noisy causing by the imaging procedure, and the image quality of PET image is affected inherently. To improve this issue, a novel nonlinear anisotropic diffusion technique based on the diffusion theorem with multi-scale and edge detection scheme to inhibit the noise level and hold the boundary characteristics of the high noise PET image was provided in this paper.