基于多重分形谱和自组织神经网络的医学图像分割

背景:单一的多重分形谱图像分割虽然在边缘及纹理的区分上有较大优势,但是选择不同的测度,不同的闽值对于分割结果影响比较大,正确地选择最优的测度比较困难.目的:结合多重分形谱图像分割法及自组织特征映射神经网络对医学图像进行处理.方法:以图像每一象素及其周围象素的均值及方差为基本特征,再结合4种不同多重分形谱为纹理特征,实现自组织特征映射神经网络.结果与结论:选择不同的测度对同一图像的分割结果是不一样的,并且同一种测度对不同图像的分割效果也不一样,说明基于多重分形谱的医学图像分割中选择合适的测度是一个关键所在.因此将多重分形谱结合自组织特征映射神经网络的方法对图像进行处理,该方法省略了选择测度的步骤,直接把4种多重分形谱作为特征,与另两种基本特征一起作为自组织神经网络的输入,对网络进行学习,并自动对图像进行分割.实验结果表明该方法在满足复杂图像中有效进行分割的同时达到了自动、自适应的目的.     

基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

一种数字人脑部切片图像分割新方法

目的 提出一种人脑切片图像自动分割算法,以克服现有的方法对大量人工参与的依赖.方法 针对人脑切片图像的特征,提出一种基于区域生长的灰度直方图阈值化分割算法.首先通过区域生长过程对图像进行初始的粗分割,再用直方图阈值化方法进行二次细分割提取目标区域.结果 采用此方法准确有效地分割出了大脑白质和大脑皮质.结论 此算法结合切片图像的全局信息和局部信息应用于分割,是一种比较好的分割方法.     

基于对象识别和分割的图像噪声抑制

在医学图像降噪中,不损失图像细节是至关重要的.传统的方法是直接对整个图像进行滤波处理.然而这种操作在减少噪声的同时也会破坏图像的细节.因此,关键的问题就是怎样在保持图像细节的同时能够减少噪声.为了解决上面提到的问题,在这篇论文中提出了一种模拟生物视觉的图像处理方法,首先提取图像的边缘信息,将图像分割成边缘区域和非边缘区域,然后对这两个区域采取不同的滤波降噪,再进行重新合成.与传统方法相比,本方法在保存图像细节上有一定的效果.     

肝癌图像感兴趣目标的准确分割

背景:Snake模型为医学图像分割提供了一个全新的分割方式,可以克服传统图像分割方法在医学图像分割中的缺点.目的:针对肝癌CT图像特点,提出了一种改进的B样条曲线的Snake模型图像分割算法.方法:对腹部CT图像进行预处理,获得肝脏癌变部分的初始轮廓,再构造闭合B样条Snake模型,最后使用MMSE最小化外力变形模型以实现图像的准确分割.结果与结论:改进的B-Snake分割算法不仅减少了噪声的影响,而且使Snake曲线较好地收敛于目标轮廓边缘,对于肝癌CT图像该方法取得了感兴趣目标的良好分割效果.     

基于阈值分割和Snake模型的弱边缘医学超声图像自动分割

医学超声图像分割是图像处理中的一项关键技术.文章以胆结石超声图像为例,介绍一种新的弱边缘超声图像自动分割算法.首先采用基于直方图凹度分析的闽值分割方法确定Snake模型的初始蛇,再基于Snake模型结合贪婪算法对图像进行目标分割.实验结果表明该算法对弱边缘现象较为严重的医学超声图像进行目标分割时,定位准确,分割效果良好,足一种全自动的超声医学图像分割方法.     

基于相似度测量的医学超声图像对比度增强

目的探求一种基于相似度测量的医学超声图像对比度增强方法。方法本文在基于相似度测量的对比度增强方法的基础上,针对医学超声图像的特点,引入了局部模糊分形维数特征,改进了相似度和对比度变换因子的计算方法,同时提出了对比度增强系数的自适应选择方法,通过改变图像局部对比度增强图像。结果大量的实验结果表明处理后的图像对比度得到了明显的提高,同时背景和噪声未出现明显改变,组织细节信息保留完整,图像可读性增强。结论本文算法在增强图像对比度的同时能够有效地保留组织细节信息并抑制噪声过度增强,是一种有效的医学超声图像对比度增强方法。     

基于SAPSO优化三维Otsu方法的医学图像分割算法

医学图像具有内容丰富多样、特征丰富、多尺度等特性,因此对医学图像的分割比一般图像的难度更大.针对上述问题,提出了基于改进粒子群优化三维Otsu方法的医学图像分割算法.由于三维Otsu方法计算量大,采用粒了群优化算法来搜索阈值向量,每个粒子代表一个可行的阈值向量,通过粒子群之间的协作来获得最优阈值.由于粒子群优化算法容易陷入局部最优解的的缺点,提出了模拟退火的粒子群优化方法,使其能够快速准确得到整体最佳解,还能保持粒子群算法求解速度快的优点.最后通过仿真实验得出了结论表明,所提出的方法不仅能得到理想的结果,而且计算量人大减少.     

Gibbs场先验参数估计在医学图像分割中的应用

背景:基于马尔科夫随机场的图像分割算法已经成为医学图像分割的重要方法,其中,Gibbs场先验参数的取值对分割精度有很大的影响.目的:根据脑部MR图像的成像特点,探讨Gibbs场先验参数的估计方法,从而提高图像分割的精度.方法:通过对脑部MR图像的统计分析,得到图像高斯噪声的方差与Gibbs场先验参数的对应关系.然后在基于马尔可夫随机场图像分割算法的迭代过程中,根据高斯分布的方差估计值,用插值方法估计Gibbs场先验参数.结果与结论:通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明该方法比传统的设定Gibbs场先验参数为某一常数的方法有更精确的图像分割能力,并且实现了图像的自适应分割,具有方法简单、运算速度快、稳健性好的特点.     

基于维纳滤波与小波相融合的超声医学图像去噪方法

目的 探求一种有效的超声医学图像去噪方法.方法 在分析维纳滤波和基于自适应前处理的多尺度小波非线性阈值斑点噪声抑制方法(MSSNT-A)的基础上,提出一种基于维纳滤波与MSSNT-A相融合的超声医学图像去噪方法.利用该方法首先对加噪图像分别进行维纳滤波和MSSNT-A去噪.然后提取维纳滤波处理后的图像边缘,再将其与MSSNT-A去噪后的图像的_柑应像素点进行融合,得到去噪图像.结果 有效地去除了斑点噪声,图像的细节得到保留.结论 与维纳滤波和MSSNT-A方法相比,该方法在有效去除斑点噪声的同时,很好地保留了图像边缘和图像细节信息.     

Volume segmentation and reconstruction from freehand three-dimensional ultrasound data with application to ovarian follicle measurement

This article presents a semi-automatic method for segmentation and reconstruction of freehand three-dimensional (3D) ultrasound data. The method incorporates a number of interesting features within the level-set framework: First, segmentation is carried out using region competition, requiring multiple distinct and competing regions to be encoded within the framework. This region competition uses a simple dot-product based similarity measure to compare intensities within each region. In addition, segmentation and surface reconstruction is performed within the 3D domain to take advantage of the additional spatial information available. This means that the method must interpolate the surface where there are gaps in the data, a feature common to freehand 3D ultrasound reconstruction. Finally, although the level-set method is restricted to a voxel grid, no assumption is made that the data being segmented will conform to this grid and may be segmented in its world-reference position. The volume reconstruction method is demonstrated in vivo for the volume measurement of ovarian follicles. The 3D reconstructions produce a lower error variance than the current clinical measurement based on a mean diameter estimated from two-dimensional (2D) images. However, both the clinical measurement and the semi-automatic method appear to underestimate the true follicular volume.     

Fractal and multifractal analysis: A review

Over the last years, fractal and multifractal geometries were applied extensively in many medical signal (1D, 2D or 3D) analysis applications like pattern recognition, texture analysis and segmentation. Application of this geometry relies heavily on the estimation of the fractal features. Various methods were proposed to estimate the fractal dimension or multifractal spectral of a signal. This article presents an overview of these algorithms, the way they work, their benefits and their limits. The aim of this review is to explain and to categorize the various algorithms into groups and their application in the field of medical signal analysis.     

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.     

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.     

An information theoretic approach for non-rigid image registration using voxel class probabilities

We propose two information theoretic similarity measures that allow to incorporate tissue class information in non-rigid image registration. The first measure assumes that tissue class probabilities have been assigned to each of the images to be registered by prior segmentation of both of them. One image is then non-rigidly deformed to match the other such that the fuzzy overlap of corresponding voxel object labels becomes similar to the ideal case whereby the tissue probability maps of both images are identical. Image similarity is assessed during registration by the divergence between the ideal and actual joint class probability distributions of both images. A second registration measure is proposed that applies in case a segmentation is available for only one of the images, for instance an atlas image that is to be matched to a study image to guide the segmentation thereof. Intensities in one image are matched to the fuzzy class labels in the other image by minimizing the conditional entropy of the intensities in the first image given the class labels in the second image. We derive analytic expressions for the gradient of each measure with respect to individual voxel displacements to derive a force field that drives the registration process, which is regularized by a viscous fluid model. The performance of the class-based measures is evaluated in the context of non-rigid inter-subject registration and atlas-based segmentation of MR brain images and compared with maximization of mutual information using only intensity information. Our results demonstrate that incorporation of class information in the registration measure significantly improves the overlap between corresponding tissue classes after non-rigid matching. The methods proposed here open new perspectives for integrating segmentation and registration in a single process, whereby the output of one is used to guide the other.     

System Complexity As a Measure of Safe Capacity for the Emergency Department

Objectives
System complexity is introduced as a new measure of system state for the emergency department (ED). In its original form, the measure quantifies the uncertainty of demands on system resources. For application in the ED, the measure is being modified to quantify both workload and uncertainty to produce a single integrated measure of system state.
Methods
Complexity is quantified using an information-theoretic or entropic approach developed in manufacturing and operations research. In its original form, complexity is calculated on the basis of four system parameters: 1) the number of resources (clinicians and processing entities such as radiology and laboratory systems), 2) the number of possible work states for each resource, 3) the probability that a resource is in a particular work state, and 4) the probability of queue changes (i.e., where a queue is defined by the number of patients or patient orders being managed by a resource) during a specified time period.
Results
An example is presented to demonstrate how complexity is calculated and interpreted for a simple system composed of three resources (i.e., emergency physicians) managing varying patient loads. The example shows that variation in physician work states and patient queues produces different scores of complexity for each physician. It also illustrates how complexity and workload differ.
Conclusions
System complexity is a viable and technically feasible measurement for monitoring and managing surge capacity in the ED.     

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images to segment. However, due to differences between scanners, scanning parameters, and patients such a training set may be difficult to obtain.We present a transfer-learning approach to segmentation by multi-feature voxelwise classification. The presented method can be trained using a heterogeneous set of training images that may be obtained with different scanners than the target image. In our approach each training image is given a weight based on the distribution of its voxels in the feature space. These image weights are chosen as to minimize the difference between the weighted probability density function (PDF) of the voxels of the training images and the PDF of the voxels of the target image. The voxels and weights of the training images are then used to train a weighted classifier.We tested our method on three segmentation tasks: brain-tissue segmentation, skull stripping, and white-matter-lesion segmentation. For all three applications, the proposed weighted classifier significantly outperformed an unweighted classifier on all training images, reducing classification errors by up to 42%. For brain-tissue segmentation and skull stripping our method even significantly outperformed the traditional approach of training on representative training images from the same study as the target image.     

Probabilistic segmentation of white matter lesions in MR imaging

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.     

Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images

Tumor classification and segmentation are two important tasks for computer-aided diagnosis (CAD) using 3D automated breast ultrasound (ABUS) images. However, they are challenging due to the significant shape variation of breast tumors and the fuzzy nature of ultrasound images (e.g., low contrast and signal to noise ratio). Considering the correlation between tumor classification and segmentation, we argue that learning these two tasks jointly is able to improve the outcomes of both tasks. In this paper, we propose a novel multi-task learning framework for joint segmentation and classification of tumors in ABUS images. The proposed framework consists of two sub-networks: an encoder-decoder network for segmentation and a light-weight multi-scale network for classification. To account for the fuzzy boundaries of tumors in ABUS images, our framework uses an iterative training strategy to refine feature maps with the help of probability maps obtained from previous iterations. Experimental results based on a clinical dataset of 170 3D ABUS volumes collected from 107 patients indicate that the proposed multi-task framework improves tumor segmentation and classification over the single-task learning counterparts.