College of Electronic and Information Engineering, Hebei University, Baoding Hebei, China.
Industrial and Commercial College, Hebei University, Baoding Hebei, China.
Curr Med Imaging. 2020;16(10):1243-1258. doi: 10.2174/1573405616999200817103920.
Medical image fusion is very important for the diagnosis and treatment of diseases. In recent years, there have been a number of different multi-modal medical image fusion algorithms that can provide delicate contexts for disease diagnosis more clearly and more conveniently. Recently, nuclear norm minimization and deep learning have been used effectively in image processing.
A multi-modality medical image fusion method using a rolling guidance filter (RGF) with a convolutional neural network (CNN) based feature mapping and nuclear norm minimization (NNM) is proposed. At first, we decompose medical images to base layer components and detail layer components by using RGF. In the next step, we get the basic fused image through the pretrained CNN model. The CNN model with pre-training is used to obtain the significant characteristics of the base layer components. And we can compute the activity level measurement from the regional energy of CNN-based fusion maps. Then, a detail fused image is gained by NNM. That is, we use NNM to fuse the detail layer components. At last, the basic and detail fused images are integrated into the fused result.
From the comparison with the most advanced fusion algorithms, the results of experiments indicate that this fusion algorithm has the best effect in visual evaluation and objective standard.
The fusion algorithm using RGF and CNN-based feature mapping, combined with NNM, can improve fusion effects and suppress artifacts and blocking effects in the fused results.
医学图像融合对于疾病的诊断和治疗非常重要。近年来，出现了许多不同的多模态医学图像融合算法，这些算法可以更清晰、更方便地为疾病诊断提供更精细的上下文。最近，核范数最小化和深度学习在图像处理中得到了有效应用。
提出了一种基于卷积神经网络（CNN）特征映射和核范数最小化（NNM）的滚动引导滤波器（RGF）的多模态医学图像融合方法。首先，我们使用 RGF 将医学图像分解为基础层分量和细节层分量。下一步，我们通过预训练的 CNN 模型获得基本融合图像。使用具有预训练的 CNN 模型来获取基础层分量的显著特征。并且，我们可以从基于 CNN 的融合图的区域能量中计算活动水平测量值。然后，通过 NNM 获得细节融合图像。也就是说，我们使用 NNM 来融合细节层分量。最后，将基础层和细节层的融合图像进行整合，得到融合结果。
与最先进的融合算法进行比较的实验结果表明，该融合算法在视觉评估和客观标准方面具有最佳效果。
基于 RGF 和 CNN 特征映射，结合 NNM 的融合算法可以提高融合效果，抑制融合结果中的伪影和块效应。
