膜性肾病诊断的高光谱图像张量嵌入分析  

Tensor-based graph embedding for discriminant analysis of membranous nephropathy hyperspectral data

作　　者：吕蒙 陈天虹 李伟 杨悦[2] 涂天琪 李文歌[2] Lyu Meng;Chen Tianhong;Li Wei;Yang Yue;Tu Tianqi;Li Wen′ge(School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China;Department of Kidney Disease,China-Japan Friendship Hospital,Beijing 100029,China)

机构地区：[1]北京理工大学信息与电子学院,北京100081 [2]中日友好医院肾脏病科,北京100029

出　　处：《中国图象图形学报》2021年第8期1823-1835,共13页Journal of Image and Graphics

基　　金：北京市自然科学基金项目(JQ20021);国家自然科学基金项目(61922013)。

摘　　要：目的高光谱成像技术因其能够获取目标的详细空间和光谱信息,在医学领域引起了广泛关注。然而,对于识别任务来说,高光谱图像的高维特征通常会导致分类器性能不佳。因此,降维在高光谱图像分析过程中至关重要。为了在低维空间中保留医学高光谱图像的多流形结构信息并增强特征判别能力,本文提出了一种基于张量表示的拉普拉斯稀疏低秩图嵌入方法(tensor-based Laplacian regularized sparse and low-rank graph,T-LapSLRG),用于医学高光谱图像的判别分析。方法在T-LapSLRG中,基于有标签的张量样本,通过引入稀疏、低秩约束及流形正则项以构造监督张量图。张量表示用于捕获空间结构信息,稀疏和低秩约束用于保留局部和全局结构信息,流形正则项用于利用固有的几何信息并增强特征判别能力。通过引入张量图嵌入技术获取数据的低维特征并输入分类器以实现数据的分类及识别。结果实验数据采用膜性肾病数据集,通过降维方法获取数据的低维特征,使用支持向量机(support vector machine,SVM)分类器对获取的低维特征进行分类。将T-LapSLRG获得的实验结果与相关的降维方法获得的实验结果进行性能比较,以证明T-LapSLRG算法的有效性。采用4个性能指标,即各个类别的准确性、总体准确性(overall accuracy,OA)、平均准确性(average accuracy,AA)和Kappa系数衡量分类性能。T-LapSLRG在膜性肾病数据集下的OA为97.14%,AA为97.05%,Kappa为0.942,各项性能指标均优于对比方法。其中,OA高出1.40%~34.75%,AA高出1.46%~36.89%,Kappa高出0.031~0.73。此外,通过T-LapSLRG算法获得的各个患者的分类准确率均达到90%以上。结论T-LapSLRG算法在膜性肾病诊断中具有潜在临床价值。Objective Hyperspectral imaging systems have become promising auxiliary diagnostic tools for intelligent medicine in recent years,especially in disease diagnosis and image-guided surgery.Hyperspectral image(HSI)has hundreds of contiguous narrow spectral bands from visible to infrared electromagnetic spectrum.These bands provide a wealth of information to distinguish different chemical composition of biological tissue.The reflected,fluorescent,and transmitted light from tissue captured by HSI carry quantitative diagnostic information about tissue pathology.Wealthy spectral bands also contain redundancy,which not only degrades classification performance but also increases computational complexity.Thus,dimensionality reduction(DR)needs to be conducted to reveal the essence of data by discarding redundant information.However,most of the current DR methods are based on spectral vector input(first-order representation)that ignores important correlations in the spatial domain.Although some spectral-spatial joint technologies have been investigated to overcome this disadvantage,they still consider the spectral-spatial feature into first-order data for analysis and ignore the cubic nature of hyperspectral data.Thus,a novel tensor-based Laplacian regularized sparse and low-rank graph(T-LapSLRG)for discriminant analysis is proposed to preserve the original intrinsic structure information of medical hyperspectral data and enhance the discriminant ability of features.Method Sparse and low-rank constraints are suggested in the proposed T-LapSLRG to exploit local and global data structures while tensor analysis is developed to preserve the spatial neighborhood information.Multi-manifold is utilized to enhance the discriminant ability and describe the intrinsic geometric information.Consequently,the proposed method not only can preserve local and global structure information but also can utilize the intrinsic geometric information.Thus,it offers more discriminative power than existing tensor-based DR methods.Vector-based methods

关 键 词：医学高光谱图像 膜性肾病 张量 降维(DR) 图嵌入 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]