Semantic clustering for region-based image retrieval

With the proliferation of applications that demand content-based image retrieval, two merits are becoming more desirable. The first is the reduced search space, and the second is the reduced “semantic gap.” This paper proposes a semantic clustering scheme to achieve these two goals. By performing clustering before image retrieval, the search space can be significantly reduced. The proposed method is different from existing image clustering methods as follows: (1) it is region based, meaning that image sub-regions, instead of the whole image, are grouped into. The semantic similarities among image regions are collected over the user query and feedback history; (2) the clustering scheme is dynamic in the sense that it can evolve to include more new semantic categories. Ideally, one cluster approximates one semantic concept or a small set of closely related semantic concepts, based on which the “semantic gap” in the retrieval is reduced.     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

在线磁共振图像重建研究

实现了一种磁共振成像(MRI)系统的在线图像重建方案:研发数据仿真客户端和图像重建服务器端程序,模拟了实时在线磁共振图像重建;利用西门子MRI系统的ICE框架开发了数据通信客户端程序,并基于YAP框架开发了数据重建服务器端程序,实现了外部计算机接入MRI系统流水线的实时在线重建。这种重建方法有利于MRI算法的集成与应用。     

基于RBM-KNN的脑部磁共振图像分类

为加快医学图像分类速度,提高分类精确率,文中采用受限玻尔兹曼机(Restricted Boltzmann Machine,RBM)结合K近邻(K-Nearest Neighbor,KNN)分类器方法。首先构建可视层二值对隐层二值RBM,利用RBM训练得到特征提取器,该特征提取器可同时实现特征降维,然后特征提取器从像素单元直接提取图像特征,最后用KNN将特征分类,并用测试样本检验分类准确性。将文中方法用在脑部磁共振图像数据库分类中,实验结果表明,提出的方法具有良好的分类准确率,且明显高于基于单一统计特征提取的医学图像分类方法。     

Topology-preserving tissue classification of magnetic resonance brain images

This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.     

基于内容的图像检索

主要围绕基于内容的图像检索的相关技术进行了研究。设计分析了基于内容的图像检索系统的具体结构,研究了结构中各模块的具体内容。并且用Matlab软件工具基本实现了基于内容的图像检索,以手机图像为例,验证了算法的正确性。     

Determination of intracranial tumor volumes in a rodent brain using magnetic resonance imaging, Evans blue, and histology: a comparative study

The measurement of tumor volumes is a practical and objective method of assessing the efficacy of a therapeutic agent. However, the relative accuracy of different methods of assessing tumor volume has been unclear. Using T1-weighted, gadolinium-enhanced magnetic resonance Imaging (T1-MRI), Evans Blue infusion and histology we measured intracranial tumor volumes in a rodent brain tumor model (RT2) at days 10, 16 and 18 after implantation of cells in the caudate putamen. There is a good correlation between tumor volumes comparing T1-MRI and Evans Blue (r2 = 0.99), T1-MRI and Histology (r2 = 0.98) and histology and Evans Blue (r2 = 0.93). Each of these methods is reliable in estimating tumor volumes in laboratory animals. There was significant uptake of gadolinium and Evans Blue in the tumor suggesting a wide disruption of the blood-brain barrier.     

Permutation testing made practical for functional magnetic resonance image analysis

We describe an efficient algorithm for the step-down permutation test, applied to the analysis of functional magnetic resonance images. The algorithm's time bound is nearly linear, making it feasible as an interactive tool. Results of the permutation test algorithm applied to data from a cognitive activation paradigm are compared with those of a standard parametric test corrected for multiple comparisons. The permutation test identifies more weakly activated voxels than the parametric test, always activates a superset of the voxels activated by this parametric method, almost always yields significance levels greater than or equal to those produced by the parametric method, and tends to enlarge activated clusters rather than adding isolated voxels. Our implementation of the permutation test is freely available as part of a widely distributed software package for analysis of functional brain images.     

Three-dimensional segmentation and interpolation of magnetic resonance brain images

The authors propose a method for the 3-D reconstruction of the brain from anisotropic magnetic resonance imaging (MRI) brain data. The method essentially consists in two original algorithms both for segmentation and for interpolation of the MRI data. The segmentation process is performed in three steps. A gray level thresholding of the white and gray matter tissue is performed on the brain MR raw data. A global white matter segmentation is automatically performed with a global 3-D connectivity algorithm which takes into account the anisotropy of the MRI voxel. The gray matter is segmented with a local 3-D connectivity algorithm. Mathematical morphology tools are used to interpolate slices. The whole process gives an isotropic binary representation of both gray and white matter which are available for 3-D surface rendering. The power and practicality of this method have been tested on four brain datasets. The segmentation algorithm favorably compares to a manual one. The interpolation algorithm was compared to the shaped-based method both quantitatively and qualitatively.     

Ramp-preserving denoising for conductivity image reconstruction in magnetic resonance electrical impedance tomography

In magnetic resonance electrical impedance tomography, among several conductivity image reconstruction algorithms, the harmonic B(z) algorithm has been successfully applied to B(z) data from phantoms and animals. The algorithm is, however, sensitive to measurement noise in B(z) data. Especially, in in vivo animal and human experiments where injection current amplitudes are limited within a few milliampere at most, measured B(z) data tend to have a low SNR. In addition, magnetic resonance (MR) signal void in outer layers of bones and gas-filled organs, for example, produces salt-pepper noise in the MR phase and, consequently, B(z) images. The B(z) images typically present areas of sloped transitions, which can be assimilated to ramps. Conductivity contrasts change ramp slopes in B(z) images and it is critical to preserve positions of those ramps to correctly recover edges in conductivity images. In this paper, we propose a ramp-preserving denoising method utilizing a structure tensor. Using an eigenvalue analysis, we identified local regions of salt-pepper noise. Outside the identified local regions, we applied an anisotropic smoothing to reduce noise while preserving their ramp structures. Inside the local regions of salt-pepper noise, we used an isotropic smoothing. After validating the proposed denoising method through numerical simulations, we applied it to in vivo animal imaging experiments. Both numerical simulation and experimental results show significant improvements in the quality of reconstructed conductivity images.     

基于颜色直方图的图像检索技术

讨论了基于颜色直方图的图像检索中颜色空间和直方图距离的选择问题,提出了利用颜色直方图来检索图像的一般框架,建立了基于两种颜色空间(RGB和HSV)和四种直方图距离共八种图像检索算法,并且采用了两种客观评价标准对所建立算法的有效性进行评价。结果证明,在基于颜色直方图的应用中,HSV空间比RGB空间更有效,EMD距离在所有四种距离量度中性能较好,但计算量稍大,与EMD距离相比,虽交集距离性能稍有不足,但所需计算量较小。     

Application of autoregressive moving average parametric modeling in magnetic resonance image reconstruction

The modeling of data is an alternative to conventional use of the fast Fourier transform (FFT) algorithm in the reconstruction of magnetic resonance (MR) images. The application of the FFT leads to artifacts and resolution loss in the image associated with the effective window on the experimentally-truncated phase encoded MR data. The transient error modeling method treats the MR data as a subset of the transient response of an infinite impulse filter (H(z) = B(z)IA(z)). Thus, the data are approximated by a deterministic autoregressive moving average (ARMA) model. The algorithm for calculating the filter coefficients is described. It is demonstrated that using the filter coefficients to reconstruct the image removes the truncation artifacts and improves the resolution. However, determining the autoregressive (AR) portion of the ARMA filter by algorithms that minimize the forward and backward prediction errors (e.g., Burg) leads to significant image degradation. The moving average (MA) portion is determined by a computationally efficient method of solving a finite difference equation with initial values. Special features of the MR data are incorporated into the transient error model. The sensitivity to noise and the choice of the best model order are discussed. MR images formed using versions of the transient error reconstruction (TERE) method and the conventional FFT algorithm are compared using data from a phantom and a human subject. Finally, the computational requirements of the algorithm are addressed.     

Non-local total bounded variation scheme for multiple-coil magnetic resonance image restoration

In this paper, we design a variational model for restoring multiple-coil magnetic resonance images (MRI) corrupted by non-central Chi distributed noise. The energy functional corresponding to the restoration problem is derived using the maximum a posteriori (MAP) estimator. Optimizing this functional yields the solution, which corresponds to the restored version of the image. The non-local total bounded variation prior is being used as the regularization term in the functional derived using the MAP estimation process. Further, the split-Bregman iteration scheme is being followed for fast numerical computation of the model. The results are compared with the state of the art MRI restoration models using visual representations and statistical measures.     

基于改进3D UNet的前列腺MR图像分割

针对前列腺磁共振 (magnetic resonance, MR)图像边缘模糊、对比度较低,灰度值分布不均衡而导致分割精度较差的问题,提出了一种结合双路径注意力(dual path attention,DPA) 和多尺度特征聚合(multi-scale feature aggregation,MFA) 模块的改进3D UNet网络模型。首先,对数据集进行重采样和裁剪处理以适应模型输入。然后,在3D UNet网络的编码器各层引入DPA 并添加残差连接,加强特征的 编码能力。同时,在网络解码器中加入MFA模块,以充分利用空间上下文信息,增强语义信息。最后,在公开数据集PROMISE12上进行验证,所提出的模型的Dice系数为89.90%,Hausdorff 距离为9.37 mm。相比较于其他模型,所提出模型的分割结果更优,且参数量和运算量更少。     

一种分形域基于内容的图像检索方法

基于内容的图像检索是多媒体、网络通信及计算机等应用研究领域的一项关键技术。该文提出了一种在分形压缩域直接进行基于内容的图像检索方法。该方法不需要对查询图像进行分形变换，因此可以提高检索速度，降低检索复杂度。仿真结果表明，使用该文提出的方法，能够有效地进行分形域基于内容的图像检索，比较大幅度地降低了检索时间，优于试验中其他3种方法。     

Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images

Presents a new method to segment brain parenchyma and cerebrospinal fluid spaces automatically in routine axial spin echo multispectral MR images. The algorithm simultaneously incorporates information about anatomical boundaries (shape) and tissue signature (grey scale) using a priori knowledge. The head and brain are divided into four regions and seven different tissue types. Each tissue type c is modeled by a multivariate Gaussian distribution N(mu(c),Sigma(c)). Each region is associated with a finite mixture density corresponding to its constituent tissue types. Initial estimates of tissue parameters {mu(c),Sigma(c )}(c=1,...,7) are obtained from k-means clustering of a single slice used for training. The first algorithmic step uses the EM-algorithm for adjusting the initial tissue parameter estimates to the MR data of new patients. The second step uses a recently developed model of dynamic contours to detect three simply closed nonintersecting curves in the plane, constituting the arachnoid/dura mater boundary of the brain, the border between the subarachnoid space and brain parenchyma, and the inner border of the parenchyma toward the lateral ventricles. The model, which is formulated by energy functions in a Bayesian framework, incorporates a priori knowledge, smoothness constraints, and updated tissue type parameters. Satisfactory maximum a posteriori probability estimates of the closed contour curves defined by the model were found using simulated annealing.     

Deep multi query image retrieval

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy.