ArticleElectronic physician2017
A novel algorithm for PET and MRI fusion based on digital curvelet transform via extracting lesions on both images.
Article in Electronic physician, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- NFSAOA: a new approach of medical brain image fusion using neutrosophic fuzzy set and arithmetic optimization algorithm.Medical & biological engineering & computing · 2026Article
- GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.Scientific reports · 2025Article
- Medical Image Fusion using bi-dimensional empirical mode decomposition (BEMD) and an Efficient Fusion Scheme.Journal of biomedical physics & engineering · 2020Article
- Forming Optimal Projection Images from Intra-Retinal Layers Using Curvelet-Based Image Fusion Method.Journal of medical signals and sensorsArticle
- Deep Learning Approach for Fusion of Magnetic Resonance Imaging-Positron Emission Tomography Image Based on Extract Image Features using Pretrained Network (VGG19).Journal of medical signals and sensorsArticle
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3 authors.
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Abstract
BACKGROUND AND
aimMerging 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.
methodsFor 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.
resultThe 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.
conclusionThe 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.
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