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

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 effec

Diabetic Retinopathy Grading by Digital Curvelet Transform

One of the major complications of diabetes is diabetic retinopathy. As manual analysis and diagnosis of large amount of images are time consuming, automatic detection and grading of diabetic retinopathy are desired. In this paper, we use fundus fluorescein angiography and color fundus images simultaneously, extract 6 features employing curvelet transform, and feed them to support vector machine in order to determine diabetic retinopathy severity stages. These features are area of blood vessels, area, regularity of foveal avascular zone, and the number of micro-aneurisms therein, total number of micro-aneurisms, and area of exudates. In order to extract exudates and vessels, we respectively modify curvelet coefficients of color fundus images and angiograms. The end points of extracted vessels in predefined region of interest based on optic disk are connected together to segment foveal avascular zone region. To extract micro-aneurisms from angiogram, first extracted vessels are subtracted from original image, and after removing detected background by morphological operators and enhancing bright small pixels, micro-aneurisms are detected. 70 patients were involved in this study to classify diabetic retinopathy into 3 groups, that is, (1) no diabetic retinopathy, (2) mild/moderate nonproliferative diabetic retinopathy, (3) severe nonproliferative/proliferative diabetic retinopathy, and our simulations show that the proposed system has sensitivity and specificity of 100% for grading