医学图像在病灶检测、定性、脏器功能评估、血流估计等临床医学诊断以及病灶三维定位 、体积计算、手术规划等治疗中发挥重要作用。医学图像与普通图像相比，在成像机理、存储 、传输、配准、降噪等问题上具有特殊性，不适合直接搬用普通图像的处理方法。稀疏表示法在解决医学图像特征提取上存在的问题具有理论基础，但其线性组合的特性限制了表示医学图像的精度。为此，本项目将核非线性映射思想引入稀疏表示理论中，提出核稀疏表示理论框架，采用非线性表示方式提升描述医学图像局部特征的精度,解决其在医学图像处理上存在的问题。在此理论框架下，系统研究医学图像的核稀疏非线性表示理论，突破医学图像快速核稀疏表示、字典生成及高分辨重建三个关键问题，形成完善的核稀疏表示理论体系及医学图像处理方法体系，构建医学图像核稀疏表示的理论与方法的综合验证平台和评价体系。

Medical image plays an important role in clinical diagnosis including lesion detection, characterization, assessment of organ function, blood flow estimation, and so on. It also performs well on therapy including lesions three-dimensional positioning, volume calculations, surgical planning. Compared with the common image, medical image performs specially on imaging, storage, transmission, registration, noise reduction, and it is not opted to use directly current image processing methods. Sparse representation has its theory basis on solving the problems endured by medical image feature extraction. But its linear combination limits the precision of image representation. For it, this project introduces kernel nonlinear projection into spare representation theory, and proposes theory framework of kernel sparse representation. It applies nonlinear representation method to improve local feature precision of medical image. This project is to solve the problems in medical image processing. Based on this theory framework, this project is to solve systematically the kernel sparse nonlinear representation, and to solve the key problems on dictionary generatation, rapid optimization, adaptive learning and high resolution reconstruction, and to construct the theory system of kernel sparse representation, the method system of kernel sparse representation medical processing, to construct comprehensive theory and method test platform and evaluation system for medical image kernel sparse representation.