Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.     

基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高,无法满足实时系统要求的缺陷,提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化,采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数,保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度,且对噪声图像也能实现高效的分割。     

基于水平集的牙齿牙槽骨图像分割

口腔锥形束计算机断层扫描（Cone Beam Computed Tomography,CBCT）图像中牙齿及牙槽骨的分割对骨性结构的三维重建提供了基础,是实现牙齿牙槽骨三维可视化的必要步骤.本文根据牙齿及牙槽骨特点,将一种改进的势阱函数与水平集模型结合,克服以往势阱函数在部分区域出现“停止演化”或“过快演化”的缺陷,并将其应用在对牙齿牙槽骨的分割当中.采用多次小方差高斯滤波叠加的方式对图像进行序贯滤波预处理,解决单一方差高斯滤波难以有效滤除CBCT图像中噪声的问题,为准确分割提供了条件;基于序列图像相邻两张图片中同一牙齿的轮廓变化不大这一特点,以当前层的分割结果作为下一层曲线演化的初始轮廓,使得用更少的迭代次数得到相同结果,从而提高分割速度.另外,本文还将该算法应用于口腔磁共振图像中,并成功对单颗牙齿进行了分割.     

基于区域进化的区域增长图像分割

为克服经典区域增长算法中门限选择困难、分割稳定性不高与串行处理速度慢的不足,提出了基于区域进化的快速区域增长图像分割算法。引入了新的区域能量表示模型,并给出了迭代进化形式。在区域增长过程中,逐渐增加区域增长的门限,通过对能量函数的动态优化来逼近最佳分割结果。仿真实验表明,该方法能有效地避免经典区域增长算法中门限选择的困难,采用区间连通处理技术代替单一像素串行迭代处理方式,可使分割速度提高十多倍。     

Maximum likelihood subband polynomial regression for robust speech recognition

In this paper, we propose a model adaptation algorithm based on maximum likelihood subband polynomial regression (MLSPR) for robust speech recognition. In this algorithm, the cepstral mean vectors of prior trained hidden Markov models (HMMs) are converted to the log-spectral domain by the inverse discrete cosine transform (DCT) and each log-spectral mean vector is divided into several subband vectors. The relationship between the training and testing subband vectors is approximated by a polynomial function. The polynomial coefficients are estimated from adaptation data using the expectation–maximization (EM) algorithm under the maximum likelihood (ML) criterion. The experimental results show that the proposed MLSPR algorithm is superior to both the maximum likelihood linear regression (MLLR) adaptation and maximum likelihood subband weighting (MLSW) approach. In the MLSPR adaptation, only a very small amount of adaptation data is required and therefore it is more useful for fast model adaptation.     

一种用于前列腺区域分割的改进水平集算法

前列腺区域的精确分割是提高计算机辅助前列腺癌诊断准确率的重要前提.本文提出了一种新的精确的前列腺区域分割模型,分为4个步骤：首先,读取T₂加权磁共振（MR）图像;其次,利用半径为5个像素的8邻域模板（8x5）的局部二值模式（LBP）特征模板计算前列腺磁共振图像的LBP特征图;然后,利用改进的距离正则化水平集（DRLSE）模型对特征图进行分割,提取前列腺粗轮廓;最后将原始水平集能量函数进行优化,构造一个新的能量函数,提取局部灰度信息和梯度信息,并在此新的能量函数的基础上,将粗轮廓迭代演化为最终的细轮廓.本文将该模型在203组来自于国际光学与光子学学会-美国医学物理学家协会-国家癌症研究所（SPIE-AAPM-NCI）前列腺MR分类挑战数据库的T2W磁共振图像上进行了测试,并与医生手工分割结果进行了比较,结果表明本文提出模型得到的分割结果的Dice系数为0.94±0.01,相对体积差（RVD）为-1.21%±2.44%,95% Hausdorff距离（HD）为6.15±0.66 mm;与文献中现有的分割模型相比,使用本文提出的模型得到的前列腺区域分割结果更接近于手工分割的结果.     

A Texture Combined Multispectral Magnetic Resonance Imaging Segmentation for Nasopharyngeal Carcinoma

Tumor segmentation from magnetic resonance imaging (MRI) is important for volume estimation and visualization of nasopharyngeal carcinoma (NPC). In some cases, segmentation using the general multispectral (GM) method often obtained poor results due to the high false positives caused by complex anatomic structures and serious overlap in feature space. In this study, a texture combined multispectral fuzzy clustering (TCMFC) segmentation algorithm was proposed. A texture measure of T1-weighted (T1) MR image was introduced by calculating the two-order central statistical information of every pixel within a window after the window convolution operation. The texture measure and the intensities in T1 and contrast-enhanced T1 images formed the new 3-D feature vector for fuzzy clustering implemented by semi-supervised fuzzy c-means (SFCM). Testing showed that by reducing the false positives significantly, the TCMFC method achieved improved segmentation results, compared with the GM method.     

Saliency Snake: A unified framework with adaptive initial curve

Active contour model also known as Snake, is a popular approach for contour extraction and image segmentation. However, some existing active contour models are sensitive to the initial curve, which is usually required to be placed near the true object contour. To address this problem, this paper proposes a novel active contour model with adaptive initial curve, namely Saliency Snake. Taking visual saliency into consideration, prior shape information of the interested object is incorporated explicitly into the energy functional of Saliency Snake. Such improvement facilitates active contour evolution and leads to fast and automatic segmentation. Experiment results demonstrate that the proposed Saliency Snake can achieve superior segmentation performance both in terms of accuracy and efficiency.     

星空观测图像目标拖尾的自动消除

为了实现对星空观测图像中高亮目标产生的拖尾现象进行自动消除,建立了目标拖尾自动消除系统,对该系统所采用的图像背景高斯噪声分布参数估计、目标拖尾检测以及图像灰度值校正等算法进行研究。利用直方图最小二乘曲线拟合估计星空观测图像背景高斯噪声的分布参数;利用观测图像中目标拖尾现象的特征提出一种基于统计信息的拖尾检测算法;在确定目标拖尾位置的基础上对被污染的像素进行灰度值校正;利用Visual Studio 2005开发出一套星空观测图像目标拖尾自动消除系统。实验结果表明:针对16 bit,1 0241 024的星空观测图像,单帧图像处理时间约为300 ms,拖尾现象得到消除,恒星和目标等有用信息未被破坏。本文算法基本满足后续观测图像中弱小目标检测稳定、可靠、精度高等要求。     

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.     

基于改进DRLSE模型的前列腺磁共振图像分割

在图像引导下的前列腺磁共振图像分割的介入诊断与治疗具有重要意义.本文对距离正则化水平集演化（DRLSE）方法进行了改进并用于前列腺磁共振图像分割.前列腺磁共振图像中靠近膀胱一侧边界较为模糊，靠近尿道一侧及左右两侧边界较为清晰，仅用传统的梯度信息指示函数无法达到理想分割结果.本研究分别采用两个指示函数控制边界清晰段及模糊段的演化，以达到准确分割的目的.此外，还在外部能量函数中增加了能量牵制项，避免演化在虚假边界停止，驱使水平集向灰度波动较大的区域移动，并能在模糊边界停止演化.实验表明利用本方法进行前列腺磁共振图像分割的效果较好；Dice相似性系数（DSC）均值达到96%，接近专家手动分割结果.     

基于3D VNetTrans的膝关节滑膜磁共振图像自动分割

膝关节是类风湿性关节炎（Rheumatoid Arthritis,RA）常见累及关节,膝关节滑膜的精准分割对RA诊断和治疗有重要影响,本文提出了一种基于VNet网络的改进算法对膝关节滑膜磁共振图像进行自动分割．首先对39名滑膜炎患者的膝关节磁共振图像进行数据预处理,通过将Transformer编码器嵌入VNet网络底部的方式构建VNetTrans网络,使用MemSwish激活函数进行训练. 最终模型平均Dice系数为0.758 5,HD为24.6 mm;相较于VNet,Dice系数提升0.083 6,HD距离减少10 mm．实验结果表明,该算法可对膝关节磁共振图像中滑膜增生区域实现较好的3D分割,具有诊断和监测RA发展过程的应用价值．     

用于压缩感知磁共振成像的分割字典学习算法

字典学习算法可以根据数据本身的特点构建稀疏域中的基,从而使数据的表示更加稀疏.该文在传统的字典学习算法基础上提出了分割字典学习算法,由于部分磁共振图像组织结构简单、可以进行图像分割,因此可根据此特点来优化字典中基函数的构建,使磁共振图像的表达更为稀疏,从而获得更高的重建图像质量.该文利用模拟数据和真实数据进行了重建实验,结果表明与传统的字典学习算法相比,分割字典学习算法能进一步改善重建图像质量.     

结合小波融合和深度学习的脑胶质瘤自动分割

针对水肿区域边界模糊和瘤内结构复杂多变导致的脑胶质瘤分割不精确问题，本文提出了一种基于小波融合和3D-UNet网络的脑胶质瘤磁共振图像自动分割算法.首先，对脑胶质瘤磁共振图像的T1、T1ce、T2、Flair四种模态进行小波融合以及偏置场校正；然后，提取待分类的图像块；再利用提取的图像块训练3D-UNet网络以对图像块中的像素进行分类；最后加载损失率较小的网络模型进行分割，并采用基于连通区域的轮廓提取方法，以降低假阳性率.对57组Brats2018（Brain Tumor Segmentation 2018）磁共振图像测试集进行分割的结果显示，肿瘤的整体、核心和水肿部分的平均分割准确率（DSC）分别达到90.64%、80.74%和86.37%，这表明该算法分割脑胶质瘤准确率较高，与金标准相近.相比多模态图像融合前，该算法在减少输入网络数据量和图像冗余信息的同时，还一定程度上解决了胶质瘤边界模糊、分割不精确的问题，提高了分割的准确度和鲁棒性.     

Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology

Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.     

被动式太赫兹图像分割算法

提出了一种被动式太赫兹图像识别算法。算法对原始太赫兹图像进行去噪预处理之后,通过对灰度直方图分析并对其曲线拟合,选定种子点和生长阈值对图像进行区域生长,以实现图像分割。实验结果证明,算法能够有效地从被动太赫兹图像背景中提取感兴趣的目标区域,有利于快速准确地发现被检测者隐藏在衣服内的违禁品,增强了安检成像系统的实用性。     

Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms

In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.     

基于nnU-Net的乳腺DCE-MR图像中乳房和腺体自动分割

在乳腺动态增强磁共振（DCE-MR）图像中,乳房分割和腺体分割是进行乳腺癌风险评估的关键步骤.为实现在三维脂肪抑制乳腺DCE-MR图像中乳房和腺体的自动分割,本文提出一种基于nnU-Net的自动分割模型,利用U-Net分层学习图像特征的优势,融合深层特征与浅层特征,得到乳房分割和腺体分割结果.同时,基于nnU-Net策略,所使用的模型能根据图像参数自动进行预处理和数据扩增,并动态调整网络结构和参数配置.实验结果表明,在具有多样化参数的三维脂肪抑制乳腺DCE-MR图像数据集上,该模型能准确、有效地实现乳房和腺体分割,平均Dice相似系数分别达到0.969±0.007和0.893±0.054.     

Sparse MRI reconstruction using multi-contrast image guided graph representation

Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans.