Fusing multimodal medical images into an integrated image, providing more details and rich information thereby facilitating medical diagnosis and therapy. Most of the existing multiscale‐based fusion methods ignore the correlations between the decomposition coefficients and lead to incomplete fusion results. A novel contextual hidden Markov model (CHMM) is proposed to construct the statistical model of contourlet coefficients. First, the pair brain images are decomposed into multiscale, multidirectional, and anisotropic subbands with a contourlet transform. Then the low‐frequency components are fused with the choose‐max rule. For the high‐frequency coefficients, the CHMM is learned with the EM algorithm, and incorporate with a novel fuzzy entropy‐based context, building the fuzzy relationships among these coefficients. Finally, the fused brain image is obtained by using the inverse contourlet transform. Fusion experiments on several multimodal brain images show the superiority of the proposed method in terms of both visual quality and some widely used objective measures.

中文翻译：

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脑图像多模态融合中的统计相关模型

将多模态医学图像融合为一个集成图像，提供更多细节和丰富信息，从而促进医学诊断和治疗。大多数现有的基于多尺度的融合方法忽略了分解系数之间的相关性，导致融合结果不完整。提出了一种新的上下文隐马尔可夫模型（CHMM）来构建轮廓波系数的统计模型。首先，通过轮廓波变换将成对的大脑图像分解为多尺度、多方向和各向异性的子带。然后将低频分量与最大选择规则融合。对于高频系数，通过 EM 算法学习 CHMM，并结合新的基于模糊熵的上下文，建立这些系数之间的模糊关系。最后，融合的大脑图像是通过使用逆轮廓波变换获得的。几个多模态大脑图像的融合实验表明，所提出的方法在视觉质量和一些广泛使用的客观测量方面的优越性。