论文标题
使用基准进行图像融合的不同小波变换方法的分析和比较
Analysis and Comparison of Different Wavelet Transform Methods Using Benchmarks for Image Fusion
论文作者
论文摘要
近年来，医学图像融合领域都取得了许多研究成就。医学图像融合意味着将几种各种模态图像信息一起理解以形成一个图像以表达其信息。图像融合的目的是整合互补和冗余信息。 CT/MRI是最常见的医学图像融合之一。这些医学方式提供了有关不同疾病的信息。互补信息由CT和MRI提供。 CT提供了有关密集组织的最佳信息，MRI提供了有关软组织的更好信息。有两种图像融合的方法，即空间融合和转化融合。变换融合使用变换以表示多尺度上的源图像。本文根据小波系数的强度幅度提出了小波变换图像融合方法，并比较了该融合模型中分别实现的小波变换的五个变化。图像融合模型，使用离散小波变换（DWT），固定小波变换（SWT），整数举重小波变换（ILFT）双树复合小波小波转换（DT CWT）和双Tree Q-Shift Q-Shift Dual-Tree CWT，将其应用于多型模式图像。通过视觉和通过基准进行比较所得的融合图像，例如熵（E），峰信号与噪声比，（PSNR），均方根误差（RMSE），图像质量指数（IQI）和标准偏差（SD）计算。
In recent years, many research achievements are made in the medical image fusion field. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. CT/MRI is one of the most common medical image fusion. These medical modalities give information about different diseases. Complementary information is offered by CT and MRI. CT provides the best information about denser tissue and MRI offers better information on soft tissue. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. Transform fusion uses transform for representing the source images at multi-scale. This paper presents a Wavelet Transform image fusion methodology based on the intensity magnitudes of the wavelet coefficients and compares five variations of the wavelet transform implemented separately in this fusion model. The image fusion model, using the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT), the Integer Lifting Wavelet Transform (ILFT) the dual-tree Complex Wavelet Transform (DT CWT) and dual-tree Q-shift dual-tree CWT, is applied to multi-modal images. The resulting fused images are compared visually and through benchmarks such as Entropy (E), Peak Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Image Quality Index (IQI) and Standard deviation (SD) computations.
