目的 探讨基于CT平扫的深度学习自动分割模型对肾积水病人分侧肾功能评估的价值.方法 回顾性收集2 所医院共 209 例肾积水病人的平扫CT影像、年龄、性别、体质量指数(BMI)以及基于单光子发射计算机体层成像(SPECT)测量的肾脏肾小球滤过率(GFR),并以其来源医院确定为训练集(137 例)和测试集(72 例).采用U-Net方法构建肾脏自动分割模型,用于自动分割平扫CT影像上肾积水和肾实质区域,计算肾积水、肾实质体积及两者的体积比作为肾脏形态特征.根据GFR值将肾功能分为正常[GFR≥30 mL/(min·1.73 m2)]与异常[GFR＜30 mL/(min·1.73 m2)].使用多因素逻辑回归筛选独立预测特征并建立分侧肾功能评估模型.采用Dice相似性系数(DSC)评价自动分割结果,采用受试者操作特征曲线下面积(AUC)评价分侧肾功能评估模型的效能,使用DeLong检验比较AUC的差异.结果 影像采用自动分割平均耗时为每例病人 2.2 s,而手动分割耗时是自动分割的 671.8 倍.肾实质和肾积水自动分割的平均DSC分别为0.89 和 0.63.基于肾积水和肾实质的体积比、肾实质体积、年龄、BMI构建的分侧肾功能评估模型在测试集上的AUC为 0.809.基于自动分割与手动分割的分侧肾功能评估模型效能差异无统计学意义(P>0.05).结论 基于CT平扫和深度学习的肾脏自动分割模型在肾积水病人分侧肾功能评估中具有较好的价值,有望提高诊断效率.

磁共振指纹(MRF)技术是一种新的基于MRI的定量成像方法,使用伪随机采集方法,同时从不同组织获得具有唯一属性的"指纹",并使用模式识别进行后处理,得到感兴趣区的指定参数量化图.与传统MRI比较,MRF具有信噪比和效率更高能进行多参数定量测量等优势,可应用于大部分传统MRI的应用场合.文中对MRF技术在脑组织定量成像方面的研究进展作综述,对基于MRF的脑组织参数测量和MRF在脑疾病中的应用等方面进行归纳和总结.

随着计算设备的发展与算法的不断改进,人工智能在医学影像领域得到了深入的应用,有望进一步提升临床诊断效率与准确性.该文聚焦中枢神经系统疾病影像学应用领域,综合描述并分析人工智能在脑肿瘤、脑血管疾病和神经退行性疾病影像学中的应用,总结人工智能在应用中存在的问题,展望人工智能与医学影像领域结合的发展前景.

颈动脉斑块的识别和成分诊断,对于斑块稳定性的评估、引发脑卒中等事件的风险预测和治疗方案的选择等具有重要意义.机器学习算法通过自动识别影像学颈动脉斑块以及判断斑块内主要成分,可以缓解医生的视觉负担、降低工作量、减轻医疗压力,具有重要的潜在应用价值.文中综述机器学习算法在影像学颈动脉斑块分类中的研究进展,就机器学习算法在颈动脉斑块分类中的研究流程、算法和文献进行总结和归纳.

Abstract An improved feature selection method has been presented, which is based on Transformed Divergence (TD) considering weights of classes and Pearson’s correlation analysis. Using the improved method, this study evaluated several derived vegetation indices and texture measures based on Landsat-8 OLI data to determine their effect on improving the land cover classification separability in Jiangle county, Sanming city of Fujian province, China. The best vegetation indices combination was selected by the improved feature selection method and likewise, best textural combinations at different spatial resolution levels from multi-spectral bands or panchromatic band were obtained. The improved feature selection method found that a single feature could not maximize the separability of vegetation classes. When selecting four vegetation indices, the separability of vegetation classes can be maxmized significantly; and two textural measures were suitable for maxmizing the separability of vegetation classes. Overall, the result verifies that the feature selection method considering weights of classes and Pearson’s correlation coefficient can select optimal features to maximize class separability.

Hyperspectral unmixing is an important issue in hyperspectral image processing. In this paper, we transform the unmixing problem into a constrained nonlinear least squares (CNLS) problem by introducing the abundance sum-to-one constraint, abundance nonnegative constraint, and bound constraints on nonlinearity parameters. The new CNLS-based algorithms assume that the mixing mechanism of each observed pixel can be described by two forms. One is a sum of linear mixtures of endmember spectra and nonlinear variations in reflectance, and the other is a joint mixture resulting from the linearity and nonlinearity in hyperspectral data. For the former, an alternating iterative optimization algorithm is developed to solve the problem of CNLS. As for the latter, the structured total least squares optimization approach is used to obtain the abundance vectors and nonlinearity parameters simultaneously. Current mixing models can be interpreted by either or both of these two mechanisms. A comparative analysis based on Monte Carlo simulations and real data experiments is conducted to evaluate the proposed algorithms and five other state-of-the-art algorithms. Experimental results show that the proposed algorithms give outstanding performance of hyperspectral nonlinear unmixing for both synthetic data and real hyperspectral images, as satisfactory accuracy in term of abundance fractions and low computational complexity are observed.

Change detection for multitemporal hyperspectral images (HSIs) involves two major steps: change feature extraction and classification. For the first part, conventional methods mostly consider spectral features but neglect spatial patterns. Since multitemporal HSIs consist of four dimensions (one for time, one for spectral domain and two for spatial domain), we propose using 4-dimensional Higher Order Singular Value Decomposition (4D-HOSVD) based on tensor algebra to capture the details in all the dimensions simultaneously and thus producing comprehensive change features. To emphasize on the effectiveness of the change feature extraction method, this paper reduces the change classification to a simple binary problem: a pixel is either changed or unchanged. Experimental results show that 4D-HOSVD can outperform its matrix counterpart, Principal Component Analysis (PCA), as well as some other widely adopted method.

There are two great challenges for classification of hyperspectral images (HSIs): lack in prior knowledge and serious internal-class variability. To address the issues, we propose a novel semisupervised method based on affinity scoring (AS). It can harness the fuzzy state of the contributions of spectral and spatial features to classification. The method consists of three major steps: over-segmentation, semisupervised classification and modification. First, superpixels are generated to maintain local class consistency, which can balance spectral variability. Then unlabeled samples are classified by AS in an iterative manner, whereas precious labeled samples are made most use of. Finally, AS is adopted again to refine the classification map, which further exploits spatial smoothness in HSIs. Experiments show that the proposed method can largely outperform several state-of-the-art classifiers.

In recent years, band selection is becoming a popular approach to reduce the dimensionality of hyperspectral data while preserving the desired information for target detection and classification analysis. This letter presents a new method for unsupervised band selection by transforming the hyperspectral data into complex networks. By analyzing the networks' topological feature corresponding to each band, one can easily evaluate the statistical characteristics and intrinsic properties of the signals. The proposed method searches for the network set which is most qualified for demarcating and identifying different substance signatures, and then, the network set's corresponding bands are regarded as the descried output results. This network measure is a new criterion for band selection. Experimental results demonstrate that the proposed method can acquire satisfactory results when compared with traditional methods.

This paper proposed a new approach to estimate the abundance of each endmember at each pixel using distance geometry concepts and distance geometry constraints. It improves current hyperspectral unmixing algorithms in several aspects. Firstly, denoting the distance relationship with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of observation pixels, and the computation is independent of number of bands. Secondly, by the distance geometry constraint, the geometric structure of dataset is considered to obtain the optimal result with least geometric deformation. The synthetic and real data experimental results demonstrate that this algorithm is a fast and accurate algorithm for the hyperspectral unmixing.

Object counting gains an important role in many fields, such as estimating the number of cells in a microscopic image or predicting the number of pedestrians in surveillance video frames. Many researches have been done to accurately estimate the count, among which the density estimation framework first estimates the density image,then integrates probability over the whole density image to get the counting number. Under this framework, this paper proposes an algorithm based on minimizing square error to infer the density image. This algorithm has an analytical solution, and can reduce training time in a computationally efficient and stable manner. The prediction result has a competitive error rate to other density estimation algorithms. Combined with the technique of neighbor feature smoothing, the estimated density image is very similar to ground truth density in human vision.

Using distance geometry concepts and distance geometry constraints, this paper proposes a new abundance estimation method for hyperspectral unmixing, which improves current hyperspectral unmixing algorithms in several aspects. Firstly, considering the geometric structure of dataset by the distance geometry constraint, the optimal result with least geometric deformation can be obtained. Secondly, the Cayley-Menger matrix is introduced to denote the pairwise distances between the observation pixels and endmembers, which make it easy to calculate the barycentric coordinates and the computation is independent of number of bands. A series of synthetic and real data experimental results demonstrate that this algorithm is an accurate and fast algorithm for the hyperspectral unmixing.

Hyperspectral imagery contains hundreds of spectral bands, which generates a rather large amount of data, so band selection is often adopted for hyperspectral image analysis. This paper presents a novel approach for unsupervised band selection by transforming the hyperspectral data into complex networks and analyzing the corresponding topological characteristics. The networks' statistical properties are investigated to evaluate different spectral bands. The objective of the method is to find the bands which can form the most representative network formation. This is a completely new criterion for band selection. Meanwhile, the proposed technique has both an explicit physical meaning and simple process. Experimental results demonstrate that the proposed feature selection approach can acquire better results with respect to the traditional methods.

In the linear unmixing of hyperspectral images, the observation pixels form a simplex whose vertices correspond to the endmembers, hence finding the endmembers is equivalent to extracting these vertices. A common technique for determining vertices is to analyze the simplex volume, but it usually has a high computational complexity, resulting from the exhaustive searching of volume in the large hyperspectral data. This problem limits the practicability and real-time application. In this paper, we utilize triangular factorization (TF) to calculate the volume, deducing a method named simplex volume analysis based on TF (SVATF). It requires just one comparison through the data to succeed in finding the global optimal solution for all the endmembers, thus improving the searching efficiency. Dimensionality reduction transformation is not necessary, which is another advantage of this method. Moreover, since TF is a broad conception including different methods, SVATF is a framework including various implementations. Based on TF, we also propose a fast learning algorithm named abundance quantification based on TF to estimate the abundances, which further saves the computation by utilizing the intermediate values involved in SVATF. The abundance estimation method can rectify possible errors in the given endmembers by utilizing two important constraints (abundance nonnegative constraint and abundance sum-to-one constraint) of the linear mixture model, so it is useful for the imagery without pure pixels. Experimental results on synthetic and real hyperspectral data demonstrate that the proposed methods can obtain accurate results with much lower computational complexity, with respect to other state-of-the-art methods.

The hyperspectral bands are contiguous and highly correlated spectral bands. Band selection is often used to reduce the computational complexity for hyperspectral images. We proposed a new method for unsupervised band selection by using complex network to represent the spectral bands. The method completes the task with the objective of preserving the maximal information from original data in the selected bands. Both the divergences and connections between each hyperspectral band can be revealed from the topological characteristics of the generated network. We use the network topology as the criterion to identify the bands, and select the bands that can form the most approximate network comparing to the network of the original data. Experimental results demonstrate that, compared with traditional methods, the proposed algorithm can obtain accurate results with clear physical meaning and simple process.

Endmember extraction is a process to identify the spectra of materials from the hyperspectral scene. This paper presents a framework for endmember extraction by exploiting the ideas that: the endmembers are the vertices of the simplex, and the calculation of simplex volume can be simplified by triangular factorization. Triangular factorization is a broad conception including many methods, so the proposed framework is a group of methods including different implementations. Experimental results on both synthetic and real hyperspectral data demonstrate that the proposed algorithm can obtain the results with better accuracy and much lower complexity, comparing to other state-of-the-art approaches.

Nonnegative matrix factorization (NMF) has been recently applied to solve the hyperspectral unmixing problem because it ensures nonnegativity and needs no assumption for the presence of pure pixels. However, the algorithm has a large amount of local minima due to the obvious nonconvexity of the objective function. In order to improve its performance, auxiliary constraints can be introduced into the algorithm. In this paper, we propose a new approach named abundance separation and smoothness constrained NMF by introducing two constraints, namely, abundance separation and smoothness, into the NMF algorithm. These constraints are based on two properties of hy- perspectral imagery. First, usually, every ground object presents dominance in a specific region of the entire image scene and the correlation is weak between different endmembers. Second, mov- ing through various regions, ground objects usually vary slowly and abrupt changes rarely appear. We also propose a learning algorithm to further improve the performance of our method, from which the auxiliary constraints are removed at an appro- priate time. The proposed algorithm retains all the advantages of NMF and effectively overcomes the shortcoming of local minima at the same time. Experimental results based on synthetic and real hyperspectral data show the superiority of the proposed algorithm with respect to other state-of-the-art approaches.

In recent years, independent component analysis (ICA) has been applied to unmix the hyperspectral data since it can perform without the prior knowledge of ground objects. The traditional ICA algorithm regards the extracted independent components as unmixing results, which is not reasonable for hyperspectral imagery, because different endmembers are not actually independent from each other. In order to solve this problem, a new approach, named as constrained ICA, is proposed, in which we consider “uncorrelation” instead of “independence.” Two constraints of the hyperspectral data (the abundance nonnegative and abundance sum-to-one constraints) are introduced to the ICA, changing its objective function based on independence assumption. Furthermore, we develop a technique, called as adaptive abundance modeling, to characterize the statistical distribution of the data. The model is automatically constructed according to the given data, which can encourage the algorithm that is applicable to various hyperspectral images with different statistical characteristics. The experimental results on both simulated and real hyperspectral data demonstrate that the proposed approach can obtain more accurate results with respect to existing algorithms. As an algorithm with no need of prior spectral knowledge, our method provides an effective solution for the blind unmixing of the hyperspectral data.