本项目旨在研究一种脑部多模医学图像融合算法，用于为临床提供更为有效的辅助诊断手段。通过已有的的医学图像融合算法只能得到有限方向的边缘，未能很好解决方向细节提取问题，融合图像的轮廓不清晰，对于对比度低的医学图像融合效果不理想，且融合规则不符合人眼视觉特性。针对以上问题，本项目提出基于DLPCNN的平稳Contourlet域脑部多模医学图像融合算法。为此将研究三个主要问题：1.研究基于平稳Contourlet变换的医学图像分解方法，选择最优分解层数和最优方向数，充分挖掘待融合图像的方向信息；2.改进PCNN神经网络进行系数选择，建立简化的DLPCNN模型，利用人眼视觉特性，建立视觉特性模型激励的DLPCNN，制定迭代次数的判定准则，减少运算量；3.研究对医学融合图像的客观评价方法。解决了这三个问题，将能更好地对多模医学图像进行融合，课题相关成果将加强对多模图像信息综合分析能力，辅助临床诊断。

This project aims to study a type of brain multimodality medical image fusion algorithm to provide more effective support for clinical auxiliary diagnosis. Existing medical image fusion algorithm has been widely used, but they decompose images into limited direction edges and they are not well to resolve the direction of the details of extraction, the contours of the fusion image are not clear, fusion images are not satisfied; On the other hand, the fusion rules do not meet the human visual characteristics and lack of flexibility. The project will overcome the above shortcomings; Multimodality brain medical image fusion algorithm is based on DLPCNN in stationary Contourlet domain. Three main issues will be studied: 1.Medical images decomposition based on stationary Contourlet transform method to select the optimal decomposition level of decomposition of the image and the optimal direction, it will be able to fully tap the direction of information to be fusion image; 2.Improved neural network PCNN, the establishment of simplified DLPCNN model using human visual characteristics, visual characteristics of the model stimulates DLPCNN to develop the criteria of the number of iterations, and reduces the computation; 3. Objective assessment method of the Medical fusion images. Solve these three problems will be able to improve multimodality medical image fusion, the subject of results will strengthen the clinical diagnosis of multi-mode image information analysis capability, and it will assist in clinical diagnosis.