A Method for Enhancing PET Scan Images Using Nonlocal Mean Filter

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-09-01 DOI:10.18178/joig.11.3.282-287

Raghad Hazim Hamid, Nagham Saeed, H. M. Ahmed

引用次数: 0

Abstract

Medical images are an important source of information for both diagnosing and treating diseases. In many cases, the images produced by a Positron Emission Tomography (PET) scan are used to assess the effectiveness of a particular treatment. This paper presents a method for whole-body PET image denoising using a spatially-guided non-local means filter. The proposed method starts with clustering the images into regions. To estimate the noise, a Bayesian with automatic settings of the parameters was used. Then, only patches that belong to regions were collected and processed. The performance was compared to two methods; Gaussian and conventional Non-Local Means (NLM). The Jaszczak phantom and PET/ Computed Tomography (CT) for whole-body were involved in the benchmarking. The obtained results showed that in the Jaszczak phantom, the Signal-to-Noise Ratio (SNR) was significantly improved. Additionally, the proposed method improved the contrast and SNR compared to conventional NLM and Gaussian. Finally, the proposed method, in clinical whole-body PET, can be considered as another way of the post-reconstruction filter.

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一种利用非局部均值滤波增强PET扫描图像的方法

医学图像是诊断和治疗疾病的重要信息来源。在许多情况下，正电子发射断层扫描(PET)扫描产生的图像用于评估特定治疗的有效性。提出了一种基于空间引导非局部均值滤波的全身PET图像去噪方法。该方法首先将图像聚类成区域。为了估计噪声，使用了具有自动设置参数的贝叶斯方法。然后，只收集和处理属于区域的补丁。比较了两种方法的性能;高斯和常规非局部均值(NLM)。采用Jaszczak假体和全身PET/计算机断层扫描(CT)进行基准测试。结果表明，在Jaszczak模体中，信噪比(SNR)显著提高。此外，与传统NLM和高斯方法相比，该方法提高了对比度和信噪比。最后，该方法在临床全身PET中可作为重建后滤波的另一种方式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.