点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景：在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的：将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法：针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论：实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

基于分水岭算法的交互式三维分割方法

背景：在临床中准确对人体组织进行三维分割能提高临床诊断的准确性,但传统的分水岭算法存在过度分割问题,难以实现人体组织的三维分割。目的：为准确三维分割人体组织,减少图像中伪极小值点对图像分割的影响,提出了一种基于控制标记符分水岭的交互式三维分割方法。方法：提取CT序列图像的内部和外部标记符,以此修正梯度图像并进行分割;在此基础上,根据序列图像上下层的相似性,利用人机交互进行组织结构的三维分割。首先在第一张序列图像上手工选取感兴趣区域上的一个点,借助同一组织在连续CT序列图像上面积的重叠关系即可从三维序列图上提取出感兴趣区域。结果与结论：基于控制标记符的分水岭算法解决了直接应用梯度图像进行分割的过度分割问题,便于进一步分割图像。利用基于分水岭算法的交互式三维分割方法得到的三维分割结果经过三维可视化后可清晰、准确地反映组织的三维特征。     

基于一种改进的相位校正算法消除MRI运动伪影

背景：近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛。但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影。目的：去除MRI图像成像时产生的伪影,改善图像质量。方法：使用改进的相位矫正算法,并结合水平集算法去除图像伪影。去除伪影后使用模糊增强改善处理后图像的质量。结果与结论：实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想。     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景:在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的:将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法:针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论:实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

基于分水岭算法的交互式三维分割方法

背景:在临床中准确对人体组织进行三维分割能提高临床诊断的准确性,但传统的分水岭算法存在过度分割问题,难以实现人体组织的三维分割.目的:为准确三维分割人体组织,减少图像中伪极小值点对图像分割的影响,提出了一种基于控制标记符分水岭的交互式三维分割方法.方法:提取CT 序列图像的内部和外部标记符,以此修正梯度图像并进行分割;在此基础上,根据序列图像上下层的相似性,利用人机交互进行组织结构的三维分割.首先在第一张序列图像上手工选取感兴趣区域上的一个点,借助同一组织在连续CT 序列图像上面积的重叠关系即可从三维序列图上提取出感兴趣区域.结果与结论:基于控制标记符的分水岭算法解决了直接应用梯度图像进行分割的过度分割问题,便于进一步分割图像.利用基于分水岭算法的交互式三维分割方法得到的三维分割结果经过三维可视化后可清晰、准确地反映组织的三维特征.     

一种基于2D／3D配准的脊柱术中校正方法

背景：脊柱术前三维影像有助于诊断和治疗,术中患者体位变化将引起脊柱形态改变,致使术前影像不能反映术中实际情况,无法确保手术的顺利实施。目的：利用脊髓手术中影像校正术前脊柱模型形态。方法：实验提出了一种基于2D／3D配准的脊枉术中校正方法,借助数字影像重建技术完成术前X射线图像与CT体数据的2D／3D配准,进一步完成术中、术前X射线图像中独立椎段的特征匹配,利用上述配准结果实现术前脊柱CT模型的术中快速校正。结果与结论：采朋附有标记的颈椎标本进行实验,校正后可基本消除术前脊柱模型与术中形态的偏差,其误差可控制在1mm以内,能够满足医学临床要求。     

MR图像伪影与K-空间数据关系的研究

目的研究MRI图像和K-空间数据之间的映射关系以及因K-空间数据有问题而引起的典型的图像伪影。方法给出K-空间及K-平面的定义,通过MRI模拟实验揭示K-空间数据的不同区域所包含的信息的差异。结果给出了K-空间数据丢失或欠采样所引起的各种伪影的特征。结论MR图像与K-空间数据之间满足傅立叶映射关系,了解K-空间的性质对于理解图像伪影产生的根源、校正图像畸变和伪影直接对k-空间数据进行处理有时候更方便、更有效。     

基于插值方法去除CT图像中的金属伪影

目的：对比观察线性插值、非线性插值和三次样条插值3种方法消除金属伪影后的CT图像。 方法：首先，利用全局阈值分割的方法把原图分割为金属部分和条状伪影部分。然后用雷登变换把伪影图像变换至弦空间，即得到弦图。然后用线性插值、非线性插值和三次样条插值来对伪影弦图进行处理，得到矫正的弦图。最后利用滤波反投影法重建图像。 结果：线性插值最为明显的消除了放射状条纹伪影，非线性插值在消除条纹的时候带入了噪声，而三次样条插值对伪影的处理最弱。 结论：实验表明，在各种单一插值算法中，线性插值是最为有效的消除金属伪影的插值方法。     

一种快速校正CT环形伪影的方法

背景：环形伪影严重影响了CT图像质量,对图像后处理造成困难以及容易造成误诊断。目前去除环形伪影必不可少。目的：去除CT重建图像中的环形伪影,提高CT图像质量以及后续处理和量化分析的精度,便于诊断。方法：首先把含环形伪影的CT图像进行线性变换,将灰度图像转换成浮点类型的图像。接着由直角坐标变换到极坐标,这样原来的环形伪影就被变换成线形伪影。设计多维滤波器,计算每个象素滤波后均值以及方差,通过与闽值比较确定伪影范围。最后通过对伪影范围进行修正以及对图像进行坐标变换,变为灰度图像。结果与结论：通过Matlab7.0软件设计程序,处理含环形伪影的CT图像。实验表明,此方法能有效快速地校正CT环形伪影,是一种属于图像后处理的校正方法。     

基于孤立点分析的医学图像处理

背景：在进行临床诊断的时候,医学影像中许多微小的纹理变化细节和形态特征不容易被发现,会影响对病情的早期判断。目的：为数字医学图像中病变的计算机诊断提供一种新的思路和方法,帮助医生及早发现和诊断恶性病变、提高诊断效率和准确性。方法：运用孤立点数据挖掘技术,分析提取医学图像数据集中隐藏、不为人所注意、易被抛弃的但非常有用的信息,找出其中的医学诊断规则和模式,从而辅助医生进行疾病诊断。结果与结论：实验证明基于医学图像象素聚类的孤立点分析算法对于发现脑部病变是切实可行的。     

Feature matching for texture-less endoscopy images via superpixel vector field consistency

Feature matching is an important technology to obtain the surface morphology of soft tissues in intraoperative endoscopy images. The extraction of features from clinical endoscopy images is a difficult problem, especially for texture-less images. The reduction of surface details makes the problem more challenging. We proposed an adaptive gradient-preserving method to improve the visual feature of texture-less images. For feature matching, we first constructed a spatial motion field by using the superpixel blocks and estimated its information entropy matching with the motion consistency algorithm to obtain the initial outlier feature screening. Second, we extended the superpixel spatial motion field to the vector field and constrained it with the vector feature to optimize the confidence of the initial matching set. Evaluations were implemented on public and undisclosed datasets. Our method increased by an order of magnitude in the three feature point extraction methods than the original image. In the public dataset, the accuracy and F1-score increased to 92.6% and 91.5%. The matching score was improved by 1.92%. In the undisclosed dataset, the reconstructed surface integrity of the proposed method was improved from 30% to 85%. Furthermore, we also presented the surface reconstruction result of differently sized images to validate the robustness of our method, which showed high-quality feature matching results. Overall, the experiment results proved the effectiveness of the proposed matching method. This demonstrates its capability to extract sufficient visual feature points and generate reliable feature matches for 3D reconstruction and meaningful applications in clinical.     

A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome

To match anatomical trees such as airways, we propose a graph-based strategy combined with an appropriate distance function. The strategy was devised to cope with topological and geometrical differences that may arise between trees corresponding to the same subject, but extracted from images acquired in different conditions. The proposed distance function, called father/family distance, combines topological and geometrical information in a single measure, by calculating a sum of path-to-path distances between sub-trees of limited extent. To use it successfully, the branches of these sub-trees need to be brought closer, which is obtained by successively translating the roots of these sub-trees prior to their actual matching. The work herein presented contributes to a study of the acute respiratory distress syndrome, where a series of pulmonary CT images from the same subject is acquired at varying settings (pressure and volume) of the mechanical ventilation. The method was evaluated on 45 combinations of synthetic trees, as well as on 15 pairs of real airway trees: nine corresponding to end-expiration and end-inspiration with the same pressure, and six corresponding to end-inspiration with significantly different pressures. It achieved a high rate of successful matches with respect to a hand-made reference containing a total of 2391 matches in real data: sensitivity of 94.3% and precision of 92.8%, when using the basic parameter settings of the algorithm.     

Remote sensing image registration based on Gaussian-Hermite moments and the Pseudo-RANSAC algorithm

This study deals with an important issue that is often encountered with the registration of remote sensing images which are obtained at different times and/or through inter/intra sensors. Remote sensing images may differ significantly in gray-level characteristics and contrast, among other aspects. Thus, it may be difficult to apply directly area-based approaches which are dependent on image intensity values. In this work, a novel image registration approach based on Gaussian-Hermite moments and the Pseudo-RANSAC algorithm is proposed. The problem of intensity difference commonly incurred in multi-temporal or multimodal remote sensing image registration is tackled using features that are invariant to intensity mapping during the feature point matching process. In particular, the feature points are herein represented by a range of newly introduced Gaussian-Hermite moments, and the corresponding feature points in a certain reference image are sought with the Euclidean distance measure. Moreover, an improved RANdom SAmple Consensus (RANSAC) algorithm is presented, reducing computational time complexity while improving performance in stability and accuracy. The final warping of images according to their refined feature points is conducted with bilinear interpolation. The proposed approach has been successfully applied to register synthetic and real remote sensing images, demonstrating its efficacy with systematic experimental evaluations.     

An efficient speckle tracking algorithm for ultrasonic imaging

An efficient speckle tracking algorithm is proposed for motion estimation in ultrasonic imaging. Speckle tracking involves a matching process and a searching process. The matching process of the proposed algorithm is based on a Block Sum Pyramid algorithm that significantly reduces the computational complexity while maintaining the same accuracy as the conventional sum of absolute difference approach. The searching process, on the other hand, is based on a multilevel search strategy rather than the full-search strategy used by most conventional tracking methods. Both simulated speckle images and clinical breast images were used to test the performance of the proposed algorithm. The results show that the computation efficiency is improved by up to a factor of five over the conventional approach. The improved efficiency enables real-time or near-real-time implementation of motion estimation in ultrasonic imaging, which is particularly beneficial in areas such as blood flow estimation, elasticity imaging, speckle image registration, and strain compounding.     

随机森林滑动窗法探查类风湿疾病单核苷酸多态性及在上位显性交互研究中的应用

背景：类风湿性关节炎是一种复杂的多基因遗传疾病。很多传统的遗传学方法难以分析高通量的类风湿病数据,从而挖掘出与疾病相关的遗传标记。目的：采用数据挖掘方法,提取与类风湿病关联的新的致病＂靶点＂。方法：采用随机森林滑动窗方法探查类风湿疾病的单核苷酸多态性,先将全部单核苷酸多态性按照它们的基尼指数进行排序。然后通过滑动窗,每增加1个单核苷酸多态性,就将这些单核苷酸多态性作为分类变量计算袋外样本的分类错误率,将分类错误率达到最低时的单核苷酸多态性作为特征单核苷酸多态性。还采用多态性互作算法研究了特征单核苷酸多态性的上位显性交互及三向交互。结果与结论：结果发现由该方法识别的特征单核苷酸多态性有不少都得到了文献的验证,证实和疾病有关。同时,特征单核苷酸多态性的交互作用分析为疾病的研究提供了理论依据。     

Skin lesion tracking using structured graphical models

An automatic pigmented skin lesions tracking system, which is important for early skin cancer detection, is proposed in this work. The input to the system is a pair of skin back images of the same subject captured at different times. The output is the correspondence (matching) between the detected lesions and the identification of newly appearing and disappearing ones. First, a set of anatomical landmarks are detected using a pictorial structure algorithm. The lesions that are located within the polygon defined by the landmarks are identified and their anatomical spatial contexts are encoded by the landmarks. Then, these lesions are matched by labeling an association graph using a tensor-based algorithm. A structured support vector machine is employed to learn all free parameters in the aforementioned steps. An adaptive learning approach (on-the-fly vs offline learning) is applied to set the parameters of the matching objective function using the estimated error of the detected landmarks. The effectiveness of the different steps in our framework is validated on 194 skin back images (97 pairs).     

Multi-sensor SAR image coregistration based on stable convex corner points and double matching network

Since significant differences exist in imaging principle and parameters of different synthetic aperture radar (SAR) sensors, the detection of stable consistent features and accurate feature matching are difficult to implement in multi-sensor SAR image coregistration. To solve these problems, this study proposed a coregistration method based on stable convex corner point and double matching network. The stable convex corner points were detected from stable convex polygons, which were obtained by the proposed local stable extremal region extraction method, convex polygon fitting, and normalization. The double matching network was divided into coarse and fine matching. We utilized spatial relationships of the stable convex polygons to build a coarse matching network, and the fine matching network was formed by stable convex corner points. Precise matching was achieved with region features and spatial relations. Experimental results on multi-sensor SAR images showed that the proposed method had superior performance.     

A graph-based approach for the retrieval of multi-modality medical images

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging.The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships.We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location.We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects.     

基于正侧位锥束CT投影快速构建脊椎三维模型

目的 探讨基于C形臂的两张正侧位2D锥束CT（CBCT）投影图像进行3D模型重建的效果。方法 采用半自动化的二维投影图像特征点提取算法,选定18点的特征点集并提取其对应的正侧位二维投影图像平面坐标,然后针对C形臂CBCT系统建立坐标系,推导特征点三维空间坐标与其在投影图像中平面坐标之间的几何关系,代入转换公式获得特征点集的三维空间坐标。利用薄板样条法对三维脊椎基础模型进行空间非刚性插值获得三维脊椎目标模型。将L3石膏模型置入C形臂CBCT系统,获取375幅圆周扫描图像,利用FDK算法重建三维模型并进行表面重采样得到三维脊椎参考模型,对其进行不规则形状调制得到三维脊椎基础模型,利用本文方法对三维脊椎基础模型进行空间非刚性插值得到三维脊椎目标模型,并设置对照组对本文方法的精度进行评价。结果 相对于特征点手动提取和边缘增强提取算法,采用半自动化特征点提取算法构建获得的三维脊椎目标模型与参考模型的误差降低至1 mm以内。结论 采用本文方法可构建出近似的、精度较高的脊椎三维模型,为基于C形臂CBCT的手术导航提供3D图像支持。     

An efficient hyper-graph based tie point matching method for aerial imagery

ABSTRACT

For the aerial images characterized by low contrast or containing repetitive patterns or homogeneous textures, robust tie point matching is still a challenging task. In this letter, an effective hypergraph-based tie point matching method is proposed. Firstly, feature points are divided into several point clusters in the overlapping area of images. Secondly, a two-stage matching pipeline, that is, candidate feature point matching and high-order graph matching, is performed. The candidate relationship is utilized to establish the high-order similarity tensor without any information loss. The experimental results show that our algorithm can significantly enhance the matching robustness and success rate compared with traditional tie point matching algorithm, and when compared with state-of-art hypergraph matching methods, our algorithm can obtain approximate equal matching success rate with a largely higher computational efficiency.