Penalized maximum likelihood algorithm for positron emission tomography by using anisotropic median-diffusion
type: Item
instance of: scholarly article
title: Penalized maximum likelihood algorithm for positron emission tomography by using anisotropic median-diffusion
zbMATH Open document ID: 1407.94015
DOI: 10.1155/2014/491239
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Nowadays, positron emission tomography (PET) is widely used in engineering. In this paper, a novel penalized maximum likelihood (PML) algorithm is presented for improving the quality of PET images. The proposed algorithm fuses an anisotropic median-diffusion (AMD) filter to the maximum-likelihood expectation-maximization (MLEM) algorithm. The fusing algorithm shows its positive effect on image reconstruction and denoising. Experimental results present that the proposed method denoises and reconstructs images with high quality. Furthermore, by comparing with other classical reconstructing algorithms, this novel algorithm shows better performance in the edge preservation.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 65K10
zbMATH DE Number: 7016568
Wikidata QID: Q59067786
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/491239
OpenAlex ID: W2074717554
cites work: Noise-Driven Anisotropic Diffusion Filtering of MRI
cites work: Q3325518
description: scientific article; zbMATH DE number 7016568