Contrast enhancement (CE) of magnetic resonance (MR) brain images is an important technique to bring out the tissue details for clinical diagnosis. Recently, a new form of image enhancement has been proposed to complete the task without any information loss. Specifically, information required to restore the original image is reversibly hidden into the enhanced image. Moreover, several image segmentation based algorithms have been proposed so that the region of interest can be exclusively enhanced. However, with the reversible algorithms, it is hard to properly enhance the tissues in MR brain images when they are relatively small or connected with each other. To address this issue, a hierarchical CE scheme is proposed for MR brain images with reversibility in this letter. Firstly, a deep convolutional neural network is used to segment multiple tissue classes automatically. Then, the segmented tissues are individually utilized to guide the CE procedure so that individual-tissue-enhanced images are generated. Compared with using the background information to guide the CE procedure, better tissue enhancement effects and visual quality are both obtained by our proposed hierarchical scheme. The evaluation results obtained over MR brain test images demonstrate the reversibility and adaptability of the proposed scheme for the enhancement of interested tissues.

中文翻译：

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具有可逆性的MR脑图像中多个组织的对比度增强

磁共振（MR）脑图像的对比度增强（CE）是一种重要的技术，可将组织细节显示出来以进行临床诊断。近来，已经提出了一种新形式的图像增强来完成任务而没有任何信息丢失。具体而言，还原原始图像所需的信息可逆地隐藏在增强图像中。此外，已经提出了几种基于图像分割的算法，从而可以专门增强感兴趣区域。然而，利用可逆算法，当它们相对较小或彼此连接时，很难正确增强MR脑部图像中的组织。为了解决这个问题，在这封信中提出了具有可逆性的MR脑图像分级CE方案。首先，深度卷积神经网络用于自动分割多个组织类别。然后，将分割的组织单独用于指导CE程序，以便生成单个组织增强的图像。与使用背景信息指导CE程序相比，我们提出的分层方案均获得了更好的组织增强效果和视觉质量。通过MR脑部测试图像获得的评估结果证明了所提出的用于增强感兴趣组织的方案的可逆性和适应性。我们提出的分级方案都可以达到更好的组织增强效果和视觉质量。通过MR脑部测试图像获得的评估结果证明了所提出的用于增强感兴趣组织的方案的可逆性和适应性。我们提出的分级方案都可以达到更好的组织增强效果和视觉质量。通过MR脑部测试图像获得的评估结果证明了所提出的用于增强感兴趣组织的方案的可逆性和适应性。