Automatic detection of blood vessels from CTA images employing morphological operation

Recently, the disease arteriosclerosis has rapidly increased, and in particular the type that originates in the peripheral arteries of the hands and feet and is called arteriosclerosis obliterans (ASO). ASO is one of the typical diseases that cause chronic ischemia of the limbs and lead to obstruction of the blood flow. In diagnosing ASO, lower contrast enhanced computed tomography (CT) is useful for observing the arteries. However, it is a huge task for radiologists to separate accurately only the arteries which are involved in the disease, using the manual or semiautomatic proprietary software which is supplied for lower computed tomographic angiography (CTA). We have developed a new technique for the detection of arteries from CTA images by the use of a morphological operation. In this technique an N-Quoit filter, which is a useful filter for the detection of lung nodules, is applied to identify the arteries in CTA images. Some experimental results have shown good performance in the segmentation of arteries. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Scale‐invariant segmentation of dynamic contrast‐enhanced perfusion MR images with inherent scale selection

Selection of the best set of scales is problematic when developing signal‐driven approaches for pixel‐based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best trade‐off between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class‐conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class‐conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimized by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class‐conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scale‐invariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic magnetic resonance (MR) images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal‐driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterize spatial properties of labelled images. Copyright © 2002 John Wiley & Sons, Ltd.     

基于群稀疏理论的乳腺动态对比度增强核磁共振图像联合重建

乳腺在注射造影剂钆喷酸葡胺（Gd-DTPA）后,乳腺核磁共振（MR）图像中恶性肿瘤区域比正常或者良性区域呈现出更加快速和更强的灰度变化,因此动态对比度增强核磁共振成像（DCE-MRI）成为了医生检测和诊断乳腺恶性肿瘤的重要工具。但是DCE-MR图像的快速获取目前仍然是一个难题, 为了快速高效地获取这样的DCE-MR图像, 根据群稀疏思想和压缩感知（CS）理论,提出了一种结合变密度随机采样的共轭梯度下降方法。该方法首先使用变密度随机采样的方式从图像的局部k-空间（傅立叶系数）数据中获取采样信息,再将传统的基于l1范数的共轭梯度下降算法扩展到l2,1范数以使得改进的共轭梯度下降算法可以对多幅DCE-MR图像同时进行联合重建。实验结果表明:采样率小于40%时,改进的联合重建方法比多测量向量（MMV）算法在重建时间上减少了约30%;变密度随机采样比均匀随机采样在重建准确率上提高了约70%。     

脊柱MR图像自动分割方法的研究

脊柱磁共振（magnetic resonance,MR）图像精确分割是脊柱配准、三维重建等技术的前提。传统脊柱MR图像分割方法过程繁琐,精度低。为克服传统方法弊端,提出了一种基于深度学习的脊柱MR图像自动分割方法。该方法构建对称通道卷积神经网络提取多尺度图像特征,通过残差连接解决训练中网络退化问题,同时用跳跃连接层连接中间层特征减少信息丢失。在搭建的网络模型中加入卷积块注意力机制关注空间和通道中的有效特征。实验结果表明,该模型在测试集上的平均DSC系数为0.861?9,相比FCN、U-Net、DeeplabV3+和UNet++网络模型分别提高了15.34%、7.08%、5.79%、3.1%。该模型可应用于临床实践中提升脊柱MR图像的分割精度。     

基于t-混合模型的脑MR图像白质分割

核磁共振成像（MRI）作为临床辅助诊断和研究的重要工具,MR图像分割的准确性直接影响着后续处理的正确性和有效性。在目前的图像分割算法中,基于t-混合模型的图像分割方法因其快速和稳健性而受到重视。该方法的一般过程是先估计混合模型的参数,计算图像中每点的后验概率,然后根据贝叶斯最小错误率准则对图像进行分割。根据MR图像的特点,提出了基于t-混合模型的大脑MR图像白质分割的算法,并取得了较好的实验结果。     

基于分类蚁群算法的彩色图像自动分类

为提升图像自动分类算法的通用性和鲁棒性,加快算法收敛速度,针对图像分类的特点,对传统蚁群算法进行改进,引入分类蚁群模型。随机蚂蚁识别统计图像类别,构建类别表,确定聚类中心;智能蚂蚁按相应的搜索前进策略进行分类。相比基本蚁群分类算法,该算法可以在较短的时间内完成图像的自动分类。     

基于SIFT特征匹配的监控图像自动拼接

针对不同摄像头的监控图像,提出了一种优化的SIFT特征匹配的监控图像自动拼接方法。在图像整合方面,通过高速提取SIFT特征描述符并进行稳定精确匹配,利用改进RANSAC算法去除错配,从而确定待拼接图像之间的变换参数;在图像融合方面,有效消除了颜色和光照差异,最终实现自动的无缝拼接系统。实验结果证明该方法对重叠区域小、形变大、有运动遮挡和噪声的监控图像有较完美的拼接效果。     

基于细胞神经网络的生物芯片图像实时处理

运用CNN设计了一套生物芯片样点识别算法。算法实现的目标:改善已有方法的缺陷,达到良好的图像质量增强效果;将CNN输出的模拟信号图像转化为样点数据信息,使得后续的信息分析成为可能。最后利用实际CNN芯片参数估算了整套算法的运算时间,结果显示其速度达到实时处理的标准。     

基于U-net模型的全自动鼻咽肿瘤MR图像分割

鼻咽肿瘤生长方向不确定，解剖结构复杂，当前主要依靠医生手动分割，该方法耗时久同时严重依赖于医生的经验。针对这一问题，基于深度学习理论，提出一种基于U-net模型的全自动鼻咽肿瘤MR图像分割算法，利用卷积操作替换原始U-net模型中的最大池化操作以减少特征信息的损失。首先，从所有患者的肿瘤切片中提取大小为128×128的区域作为数据样本；然后，将患者样本分为训练样本集和测试样本集，并对训练样本集进行数据扩充；最后，选择训练样本集中所有数据用于训练网络模型。为了验证所提模型的有效性，选取测试样本集中患者的所有肿瘤切片进行分割，最终平均分割精度可达到：DSC（Dice Similarity Coefficient）为80.05%，PM系数为85.7%，CR系数为71.26%，ASSD（Average Symmetric Surface Distance）指标为1.1568。与基于图像块的卷积神经网络（CNN）相比，所提算法DSC，PM（Prevent Match）、CR（Correspondence Ratio）系数分别提高了9.86个百分点、19.61个百分点、16.02个百分点，ASSD指标下降了0.4364；与全卷积神经网络（FCN）模型及基于最大池化的U-net网络相比，所提算法的DSC、CR系数均取得了最优结果，PM系数较两种对比模型中的最大值低2.55个百分点，ASSD指标较两种对比模型中的最小值略高出0.0046。实验结果表明，所提算法针对鼻咽肿瘤图像可以实现较好的自动化分割效果以辅助医生进行诊断。     

Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients

This paper describes a new method for contrast enhancement in images and image sequences of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means of a shrinkage function, thus preventing noise amplification. The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy imagery, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output data.     

Automatic estimation of asymmetry for gradient-based alignment of noisy images on Lie group

Many parametric image alignment approaches assume equality of the images to register up to motion compensation. In presence of noise this assumption does not hold. In particular, for gradient-based approaches, which rely on the optimization of an error functional with gradient descent methods, the performances depend on the amount of noise in each image. We propose in this paper to use the Asymmetric Composition on Lie groups (ACL) formulation of the alignment problem to improve the robustness in presence of asymmetric levels of noise. The ACL formulation, generalizing state-of-the-art gradient-based image alignment, introduces a parameter to weight the influence of the images during the optimization. Three new methods are presented to estimate this asymmetry parameter: one supervised (MVACL) and two fully automatic (AACL and GACL). Theoretical results and experimental validation show how the new algorithms improve robustness in presence of noise. Finally, we illustrate the interest of the new approaches for object tracking under low-light conditions.     

Automatic extraction of roads from aerial images based on scale space and snakes

Abstract. We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by evaluating them quantitatively based on ground truth. Received: 22 July 1999 / Accepted: 20 March 2000     

基于多曲率轮廓信息的病理图像细胞核自动检测

细胞核自动检测既是病理图像分析技术的重要步骤,也是提高病理图像自动化分析准确性的主要瓶颈之一,原因在于病理切片制作存在染色分层不均、细胞核粘连或重叠等问题。为了提高细胞核检测的准确度,定义了一种基于多曲率轮廓的细胞核自动检测模型,通过多曲率方向能量滤波器提取细胞核轮廓信息。特征检测器基于boosting算法,利用不同曲率和方向轮廓特征的完备集合产生像素软分类器,获得像素的前景背景置信度和概率。最后利用均值漂移算法得到细胞核中心位置及其置信度。实验结果表明,该算法与其他细胞核检测算法相比,在生物组织结构变异、不均匀光照或染色条件下,以及细胞核粘连或部分重叠等情况下,有着较强的鲁棒性。     

基于动态自适应遗传算法的调制信号特征选择

神经网络分类器已被广泛应用在自动模式识别中。降低输入数据特征维数对其结构的简化和性能的提高至关重要。简单遗传算法早熟收敛和局部搜索能力弱的缺陷,使它在特征选择中的效果不理想。提出基于进化群体中值信息的动态自适应遗传算法。仿真结果表明,该算法优选特征子集速度快,解的质量稳定,神经网络分类器的识别准确率有显著提高。     

基于F1值的非极大值抑制阈值自动选取方法

传统的NMS算法的过滤阈值是人为设定的,由于阈值的选取不当可能会造成漏检和误检。在应用NMS算法时,所有图像的最佳阈值不是完全相同的,根据图像自身信息的不同而发生变化。针对上述问题,提出基于F1值的非极大值抑制阈值自动选取方法,综合考虑检测算法的准确率与召回率,选取使F1值最高的最佳过滤阈值,构建映射关系。测试阶段,利用映射关系和图像信息自动选取对应的过滤阈值。实验结果表明,本文提出的改进版本NMS算法将检测精度mAP值提高了1.1%。与现有的先进算法做对比,证明了本文算法的有效性。     

结合ASM及特征融合策略的Tagged MR左心室分割

提出了一种基于ASM框架的Tagged MR图像左心室分割方法。即从基于典型相关分析的特征融合角度对LM滤波器组提取的Tagged MR图像左心室纹理特征用典型相关分析进行优化组合,再用SVM构造分类器,通过分类器来确定边缘点,驱动ASM模型边界变形得到分割结果。通过典型相关分析的特征融合可以降低分类错误率,提高分类性能;用分类器代替经典ASM模型的基于轮廓灰度的匹配法来确定边缘点具有较强的鲁棒性。该方法在不同时刻不同断层Tagged MR图像上进行了验证,实验结果表明该方法具有较高的准确度和较强的鲁棒性。     

动态核的形态联想记忆网络在灰度图中的应用

介绍了应用于灰度图像的联想记忆和识别的动态核方法,给出了动态核选择的原则和途径。利用动态核可以解决灰度图像在含有随机噪声时的自联想记忆和识别问题,从而给出了一种较好地处理含噪灰度图像恢复的途径。通过实验,验证了该方法的良好性能,取得了较理想的结果。     

自适应滤波结合改进T-snake模型的甲状腺超声图自动分割方法

提出一种基于T-snake模型的甲状腺超声波图像分割的新方法。首先,结合基于窗口的各向异性扩散滤波方法与自适应加权中值滤波算法有效地消除甲状腺超声波图像斑点噪声;其次,以传统T-snake模型为基础,增加自适应区域能量和膨胀力对非连续边界与弱边界进行有效提取,实现甲状腺超声波图像的自动分割;最后设定模型参数,使用临床数据进行实验。结果证明,应用该方法得到自动分割结果的平均相对差异度小于5%,平均相对重叠度大于91%,验证了其可行性。     

基于级联Vnet-S网络的CT影像单一器官自动分割算法

为了快速准确地对计算机断层扫描（CT）影像中的器官进行分割，提出基于级联Vnet-S网络的单一器官自动分割算法。首先，使用第一个Vnet-S网络对CT影像中的器官进行粗分割；然后，选择分割结果中的最大连接通量做两次膨胀，根据膨胀后的最大连接通量确定器官边界并提取器官区域；最后，使用第二个Vnet-S网络对器官进行细分割。为了验证算法的性能，采用MICCAI 2017 Liver Tumor Segmentation Challenge （LiTS）数据集进行肝脏分割实验，采用ISBI LUng Nodule Analysis 2016（LUNA16）数据集进行肺分割实验。级联Vnet-S算法在LiTS的70例线上测试数据上的Dice系数为0.9600，在LUNA16的288例测试数据上的Dice系数为0.9810，均高于Vnet-S网络和Vnet网络。实验结果表明，基于级联Vnet-S网络的单一器官分割算法可以准确地对器官进行分割，而且级联Vnet-S算法的计算量小于Unet网络和Vnet网络。     

Automatic cleaning and segmentation of web images based on colors to build learning databases

This article proposes a method to segment Internet images, that is, a group of images corresponding to a specific object (the query) containing a significant amount of irrelevant images. The segmentation algorithm we propose is a combination of two distinct methods based on color. The first one considers all images to classify pixels into two sets: object pixels and background pixels. The second method segments images individually by trying to find a central object. The final segmentation is obtained by intersecting the results from both. The segmentation results are then used to re-rank images and display a clean set of images illustrating the query. The algorithm is tested on various queries for animals, natural and man-made objects, and results are discussed, showing that the obtained segmentation results are suitable for object learning.