Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains
Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains
复制标题
DOI：
10.1109/jsen.2015.2465935
复制
发表时间：
2015-12-01
影响因子：
4.3
通讯作者：
Lay-Ekuakille, Aime
中科院分区：
文献类型：
--
作者：
Bhateja, Vikrant;Patel, Himanshi;Lay-Ekuakille, Aime
实现多模态医学图像融合以最小化冗余，同时从使用不同医学成像传感器获取的输入图像中增加必要的信息。唯一的目的是产生一个单一的融合图像，这可能是一个有效的临床分析更多的信息。本文提出了一种两阶段多模态融合框架，该框架使用平稳小波变换（SWT）和非下采样Contourlet变换（NSCT）域的级联组合，用于使用两种不同的医学成像传感器模态（即，磁共振成像和计算机断层扫描）。使用SWT和NSCT的级联组合的主要优点是改善最终融合图像中的移位方差、方向性和相位信息。第一阶段采用主成分分析算法在SWT域，以最大限度地减少冗余。最大融合规则，然后应用在NSCT域的第二阶段，以提高诊断功能的对比度。使用专用的融合度量进行融合图像的定量分析。所提出的方法的融合响应也与其他国家的最先进的融合方法进行了比较，描绘所获得的融合结果的优越性。
Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.