A novel algorithm for PET and MRI fusion based on digital curvelet transform via extracting lesions on both images

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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“…Multi-scale geometric analysis (MSGA) [193] has overcome these limitations, thanks to its proposed techniques which allow multi-scale decomposition for high dimensional signals. Citing the ridgelet, curvelet, bandlet, brushlet and contourlet techniques [247][248][249][250][251][252]276]. The contourlet technique [193,250,276] is a lean 2D representation for 2D signals.…”

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