稀疏形状先验的脑肿瘤图像分割  

Brain tumor segmentation based on prior sparse shapes

作　　者：雷晓亮 于晓升 迟剑宁 王莹 吴成东 Lei Xiaoliang;Yu Xiaosheng;Chi Jianning;Wang Ying;Wu Chengdong(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Faculty of Robot Science and Engineering,Northeastern University,Shenyang 110819,China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]东北大学机器人科学与工程学院,沈阳110819

出　　处：《中国图象图形学报》2019年第12期2222-2232,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61701101,U1713216,61803077);国家重点机器人工程项目(2017YFB1300900,2017YFB1301103);中央高校基本科研业务费专项资金项目(N172603001,N181602014,N172604004,N172604003,N172604002)~~

摘　　要：目的在脑部肿瘤图像的分析过程中,准确分割出肿瘤区域对于计算机辅助脑部肿瘤疾病的诊断及治疗过程具有重要意义。然而,由于脑部图像常存在结构复杂、边界模糊、灰度不均以及肿瘤内部存在明暗区域的问题,使得肿瘤图像分割工作面临严峻挑战。为了克服上述困难,更好地实现脑部肿瘤图像分割,提出一种基于稀疏形状先验的脑肿瘤图像分割算法。方法首先,研究脑部肿瘤图像的配准与形状描述,并以此为基础构建脑部肿瘤的稀疏形状先验约束模型;继而,将该稀疏形状先验约束模型与区域能量描述方法相结合,构建基于稀疏形状先验的能量函数;最后,对能量函数进行优化及迭代,输出脑部肿瘤区域分割结果。结果本文使用脑胶质瘤公开数据集BraTS2017进行算法测试,本文算法的分割结果与真实数据之间的平均相似度达到93.97%,灵敏度达到91.3%,阳性预测率达到95.9%。本文算法的实验准确度较高,误判率较低,鲁棒性较强。结论本文算法能够结合水平集方法在拓扑结构描述和稀疏表达方法在复杂形状表达方面的优势,同时由于加入了形状约束,能够有效削弱肿瘤内部明暗区域对分割结果造成的影响,从而更准确和稳定地实现脑部肿瘤图像分割。Objective In the process of analyzing brain tumor images, accurate segmentation of brain tumors is crucial to the diagnosis and treatment of computer-aided brain tumor diseases. Magnetic resonance imaging(MRI) is the primary method of brain structure imaging in clinical applications, and imaging specialists commonly outline tumor tissues from MRI images manually to segment brain tumors. However, manual segmentation is laborious, especially when the brain image has a complex structure and the boundary is blurred. The brain tumor area in the image might have bright or dark blocks that are marked in magenta. These areas may cause holes in the result or excessive shrinkage of the contour. Moreover, due to the limitation of the imaging principle and the complexity of the human tissue structure, this technique usually encounters problems, such as uneven intensity distribution and overlapping of tissues. The segmentation effect of traditional methods based on thresholds, geometric constraints, or statistics is poor and adds challenges to tumor image segmentation. To overcome these difficulties and realize improved segmentation, the common characteristics of the brain tumor’s shape are studied to construct a sparse representation-based model and propose a brain tumor image segmentation algorithm based on prior sparse shapes. Method The Fourier-Melli method is utilized to implement image registration, and the shape description of brain tumor images is studied. A prior sparse shape constraint model of brain tumors is proposed to weaken the influence of light and dark areas inside the tumor on the segmentation results. The K-means method is used to cluster the data in the mapping matrix into several classes and calculate the average of each group separately to be used as a predefined sparse dictionary, and the sparse coefficients are updated through the orthogonal matching method. Then, the prior sparse shape constraint model is combined with the regional energy to construct the energy function. The following steps are im

关 键 词：脑肿瘤 图像分割 稀疏约束 形状先验 水平集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]