Abstract

Background and aim: Merging multimodal images is a useful tool for accurate and efficient diagnosis and analysis in medical applications. The acquired data are a high-quality fused image that contains more information than an individual image. In this paper, we focus on the fusion of MRI gray scale images and PET color images. 

Methods: For the fusion of MRI gray scale images and PET color images, we used lesion region extracting based on the digital Curvelet transform (DCT) method. As curvelet transform has a better performance in detecting the edges, regions in each image are perfectly segmented. Curvelet decomposes each image into several low- and high-frequency sub-bands. Then, the entropy of each sub-band is calculated. By comparing the entropies and coefficients of the extracted regions, the best coefficients for the fused image are chosen. The fused image is obtained via inverse Curvelet transform. In order to assess the performance, the proposed method was compared with different fusion algorithms, both visually and statistically.

Result: The analysis of the results showed that our proposed algorithm has high spectral and spatial resolution. According to the results of the quantitative fusion metrics, this method achieves an entropy value of 6.23, an MI of 1.88, and an SSIM of 0.6779. Comparison of these experiments with experiments of four other common fusion algorithms showed that our method is effective. 

Conclusion: The fusion of MRI and PET images is used to gather the useful information of both source images into one image, which is called the fused image. This study introduces a new fusion algorithm based on the digital Curvelet transform. Experiments show that our method has a high fusion effect.

 

Keywords: Medical Image Fusion, MRI, PET, Digital Curvelet Transform
 
» HTML Fulltext    » PDF Fulltext    » doi: 10.19082/4872
Air Jordan 1

Current Issue

Volume 12, Issue 3, July-September 2020

 


 

The worldwide spread of COVID-19 as an emerging, rapidly evolving situation, and the dramatic need of urgent medicine or vaccine, has rapidly brought new hypotheses for pathophysiology and potential medicinal agents to the fore. It is crucial that the research community provide a way to publish this research in a timely manner.

To contribute to this important public health discussion, the Electronic Physician Journal is excited to announce a fast-track procedure to help researchers publish their articles on COVID-19 related subjects that fall under the broad definition of public health, internal medicine, and pharmacology. We are especially welcome to all hypotheses about the pathological basis of the COVID-19 infection and the possible characteristics of potential medicine and vaccine. Submit your manuscript here


 

The 6th World Conference on Research Integrity (WCRI) is to be held on June 2-5, 2019 in Hong Kong.

The WCRI is the largest and most significant international conference on research integrity. Since the first conference in Lisbon in 2007, it has given researchers, teachers, funding agencies, government officials, journal editors, senior administrators, and research students opportunities to share experiences and to discuss and promote integrity in research. Read more:


 

TDR Clinical Research and Development Fellowships

Call for applications

Deadline for submission: 7 March 2019, 16:00 (GMT)

TDR provides fellowships for early- to mid-career researchers and clinical trial staff (e.g. clinicians, pharmacists, medical statisticians, data managers, other health researchers) in low- and middle-income countries (LMICs) to learn how to conduct clinical trials. Read more:


Meta-Analysis Workshops in New York, USA, and London, UK, in April and May 2019

Don't miss this exceptional opportunity to learn how to perform and report a Meta-analysis correctly. Two Meta-analysis workshops are organized in April and May 2019 by Dr. Michael Borenstein in New York, USA (April 08-10, 2019) and London, UK (May 27-29).

About the Instructor

Dr. Michael Borenstein, one of the authors of Introduction to Meta-Analysis, is widely recognized for his ability to make statistical concepts accessible to researchers as well as to statisticians. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA. Read more: