HEp-2细胞间接荧光免疫图像识别方法研究

项目名称： HEp-2细胞间接荧光免疫图像识别方法研究

项目编号： No.61303189

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 李宽

作者单位： 中国人民解放军国防科学技术大学

项目金额： 21万元

中文摘要： 本项目以HEp-2细胞间接免疫图像荧光型别分类为主要研究内容，结合当前的研究现状与发展趋势，贯穿整幅图像分割、细胞特征提取及多特征融合分类三个方面。提出由粗到细的分割思路，粗粒度层面：提取细胞区域，细粒度层面：精确定位细胞边缘；探索鲁棒且区分能力强的特征提取方式，首先在分析现有纹理提取方法问题基础上，结合HEp-2细胞图像特点提出改进方案，提出最大熵多值模式和分块Gabor系数加工统计思路；其次，结合压缩感知和稀疏表示，探寻适合HEp-2细胞图像的纹理提取方法；对多种特征进行融合分类，特征层面上，使用MCCA消除不同特征间的冗余信息；决策层面上，提出基于后验概率的融合分类框架。本项目前期研究成果已编制系统参加了ICPR2012的HEp-2荧光型别分类竞赛，取得了优异成绩。本项目有广阔的应用前景，对提高我国的医学水平、保障人民群众的健康安全，有重要的现实意义。

中文关键词： 细胞图像分割；超像素块；特征选择；稀疏表示；不平衡分类

英文摘要： This project is focused on some issues related to staining pattern classification for HEp-2 indirect immunofluorescence cell images. These issues mainly include methods to accurately locate boundaries of HEp-2 cells, methods to extract discriminative texture features and fusion methods of different kinds of features. Particularly, a coarse-to-fine framework is proposed to locate the boundaries of cells. The rough cell regions are located first and then refined by some level set or GVF Snake based methods. To extract robust while discriminative features, several up-to-date feature extraction methods are investigated and improved according to the appearance of HEp-2 cell images. Two new texture descriptor, namely maximum entropy based local multiple pattern and block based Gabor coefficients statistic, are proposed. The compressed sensing and sparse encoding techniques are also analyzed to introduce some new texture feature extraction methods suitable for HEp-2 cell images. Finally, the feature fusion methods are examined at both the feature level and decision level. MCCA is used to remove the redundance between diffenrent features, trying to get more accurate classification results. A feature fusion framework based on posteriori probability classifier and AdaBoost.M1 framework is also introduced. The system whic

英文关键词： Cell Image Segmentation；Superpixel；Feature Selection；Sparse Representation；Imbalanced Learning