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.     

Snake模型在乳腺肿瘤超声图像处理中的运用

目的对乳腺超声图像中的肿瘤进行边缘提取。方法鉴于医学超声图像的信噪比较低,用经典的边缘提取算法无法得到较好的结果,因此,Snake模型作为一种基于高层信息的有效目标轮廓提取算法而引起广泛的关注。在原始的Snake模型的基础上,本文针对超声图像的特点对它进行了一些改进。结果从上海第六人民医院采集到乳腺超声图像15幅。在进行了灰度分割、形态滤波等一系列预处理后,将改进后的Snake模型运用到边缘提取中来,并在这15幅图像中得到了比较好的分割结果。结论改进后的Snake模型可以将乳腺超声图像中肿瘤的边缘较好地提取出来,为乳腺肿瘤计算机辅助诊断提供了重要依据。     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。     

基于配准与ASM方法的医学图像分割

目的随着医学图像数据的急剧增长,建立从医学图像中自动分割特定解剖结构的算法。方法首先,获取的脑图像体数据集通过与参考体数据集的配准,使对应层图像包含与参考数据相似的解剖结构;然后利用训练得到的统计形状模型自动定位、分割指定的解剖结构。结果实验表明这种算法能取得良好的分割结果。结论本文提出的基于互信息的图像配准和统计形状模型的分割算法,能够实现从体数据中自动定位解剖结构所在的图像位置并分割出目标结构。     

基于目标区域小波CT图像压缩算法

目的:在以往对二维X射线影像进行基于SPIHT的区域压缩编码算法研究的基础上,探讨基于目标区域小波SPIHT算法对三维CT图像的压缩方法。方法:利用目标区域的SPIHT小波算法压缩CT图像。通过灰度门限法分割目标和背景,检测并膨胀边缘以确定目标区域。对体数据分块,并进行快速小波变换。利用SPIHT算法对小波系数重要性进行判断、排序和分类形成多层位流结构。排序过程首先对小波系数建立空间树结构,然后沿空间方向树对系数进行重要性测试,分类过程对重要系数从高位到低位分成不同分辨率的位平面依次输出。结果:基于优化分割的SPIHT算法对CT目标区域压缩好于JPEG2000及JPEG算法,对CT图像目标区域以1bpp的高码率进行小波SPIHT压缩,背景以0.15bpp低码率JPEG压缩压缩,全图平均压缩码率为0.5bpp,目标区域的PSNR为43.2dB,目标区域PSNR比JPEG2000区域压缩算法提高0.8dB。结论:对于CT图像,基于目标区域的SPIHT小波压缩算法优于JPEG2000、JPEG和传统的SPIHT算法,并能在高压缩比情况下保证医学图像的质量。     

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.     

MR脑肿瘤图像分割与矩特征研究

目的 对MR脑肿瘤图像进行分割,并对分割的结果进行矩描述.方法 在分析当前常用的医学图像分割方法的基础上,提出一种基于形变模型的医学图像分割方法,并给出了相应的理论算法模型和实现步骤,最后用Visual C 6.0编程,并对MR脑肿瘤图像进行分割实验.结果 本文分割方法分割边界清晰,总体不确定性较小.结论 本文分割方法切实可行,分割效果较好,为进一步的MR脑肿瘤图像分析和研究提供了一种有效工具.     

图像分割在医学图像中的研究方法及应用

图像分割是指将一幅图像分解为若干互不交迭区域的集合,是图像处理与机器视觉的基本问题之一.医学图像分割是图像分割的一个重要应用领域,也是一个经典难题.本文从应用的特定角度,对近年来医学图像分割的新方法或改进算法进行综述,并简要讨论了每类分割方法的特点及应用.     

区域生长法结合多竞争最小二乘拟合算法去除乳腺X线摄影图像中胸大肌影

目的 评价区域生长法结合多竞争最小二乘拟合算法去除数字乳腺X线摄影（MG）图像中胸大肌影的价值。方法 分层抽样法随机抽取244例MG数据,对图像进行轮廓选择、增强数据特征、胸大肌边界轮廓粗定位和去噪处理;结合最小二乘法改进区域生长法,拟合胸大肌的边界轮廓函数,使用最优轮廓函数制作胸大肌掩膜图,计算预测图与人工勾画图交并比（IOU）及像素精度（PA）,评价其去除MG图像中的胸大肌影的价值。结果 基于上述方法所获胸大肌轮廓较为平滑,较少漏分割或过度分割,结果误差较小;还原胸大肌边界轮廓与手动分割结果非常接近,平均IOU为（89.76±4.28）%,平均PA为（89.98±3.91）%。结论 结合区域生长法与多竞争最小二乘拟合算法可用于去除MG图像中的胸大肌影。     

Discriminant snakes for 3D reconstruction of anatomical organs

In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.     

United snakes

Since their debut in 1987, snakes (active contour models) have become a standard image analysis technique with several variants now in common use. We present a framework called "United Snakes", which has two key features. First, it unifies the most popular snake variants, including finite difference, B-spline, and Hermite polynomial snakes in a consistent finite element formulation, thus expanding the range of object modeling capabilities within a uniform snake construction process. Second, it embodies the idea that the heretofore presumed competing technique known as "live wire" or "intelligent scissors" is in fact complementary to snakes and that the two techniques can advantageously be combined by introducing an effective hard constraint mechanism. The United Snakes framework amplifies the efficiency and reproducibility of the component techniques, and it offers more flexible interactive control while further minimizing user interactions. We apply United Snakes to several different medical image analysis tasks, including the segmentation of neuronal dendrites in EM images, dynamic chest image analysis, the quantification of growth plates in MR images and the isolation of the breast region in mammograms, demonstrating the generality, accuracy and robustness of the tool.     

基于CT三维重建个体化股骨假体的计算机辅助设计

背景:由于人体的绝对个性化特点,标准人工假体与患者骨骼之间的误差使二者难以很好匹配.计算机辅助设计和制造个体化假体克服了其他假体的缺点,可有效地延长人工关节的使用寿命和使用质量,并可能解决人工关节的翻修问题.国内的研究尚处于起步阶段.目的:基于CT图像的三维重建,探求个体化股骨假体计算机辅助设计在提高假体与病变骨骼匹配度中的作用.方法:CT扫描对象为1例健康男性志愿者,排除髋关节疾患.采用GE Speed Light 16排螺旋CT对股骨中上段进行层厚3 mm扫描,得到CT数据的二维图像,利用自主开发的数据格式转换软件将CT图像转换为bmp格式.对位图编辑预处理,用Mimics8.1软件进行矢量化处理,提取股骨内外轮廓.然后输入Mimics8.1和Rapidform2004三维反求工程软件中,生成股骨内外轮廓的特征曲线,重建股骨三维模型.将股骨髓腔的特征轮廓曲线dxf文件输入计算机辅助设计建模软件Solidworks2004中,以此股骨髓腔轮廓为基础,完成个体化股骨假体的设计.结果与结论:利用自主开发的数据格式转换软件,实现了CT图像信息的矢量转换.以CT二维图像为依据,进行三维反求,可获得精确的股骨内外轮廓三维实体模型.采用反求工程与正向计算机辅助设计相结合,可设计出匹配良好的个体化股骨假体.提示反求工程和计算机辅助设计技术为个体化假体的研制提供了一个有效可行的途径,解决假体与病变骨骼的良好匹配,可防止假体松动,提高其长期稳定性.     

A positive/unlabeled approach for the segmentation of medical sequences using point-wise supervision

The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To alleviate this problem, this work proposes a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. This allows annotations to be collected extremely quickly and remains applicable to numerous segmentation tasks. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objective function using sparse point-wise annotations. While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria. Our method iteratively estimates appropriate class priors and yields high segmentation quality for a variety of object types and imaging modalities. In addition, by leveraging a spatio-temporal tracking framework, we regularize our predictions by leveraging the complete data volume. We show experimentally that our approach outperforms state-of-the-art methods tailored to the same problem.     

Processing computer tomography bone data for prosthetic finite element modeling: a technical note

A software scheme is presented to extract the shapes of tibiae and fibulae from amputee computer tomography (CT) data for use in prosthetic finite element modeling. A snake algorithm is implemented to overcome challenges of bone-soft tissue edge detection common in this application. Means to enhance initial guess contours, ensure contour continuity, overcome point-clustering problems, and handle high-curvature regions are also described. Effectiveness of the algorithm is demonstrated on image data from a unilateral transtibial amputee subject.     

Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation

Segmentation of carotid artery intima-media in longitudinal ultrasound images for measuring its thickness to predict cardiovascular diseases can be simplified as detecting two nearly parallel boundaries within a certain distance range, when plaque with irregular shapes is not considered. In this paper, we improve the implementation of two dynamic programming (DP) based approaches to parallel boundary detection, dual dynamic programming (DDP) and piecewise linear dual dynamic programming (PL-DDP). Then, a novel DP based approach, dual line detection (DLD), which translates the original 2-D curve position to a 4-D parameter space representing two line segments in a local image segment, is proposed to solve the problem while maintaining efficiency and rotation invariance. To apply the DLD to ultrasound intima-media segmentation, it is imbedded in a framework that employs an edge map obtained from multiplication of the responses of two edge detectors with different scales and a coupled snake model that simultaneously deforms the two contours for maintaining parallelism. The experimental results on synthetic images and carotid arteries of clinical ultrasound images indicate improved performance of the proposed DLD compared to DDP and PL-DDP, with respect to accuracy and efficiency.     

Segmentation in Echocardiographic Sequences using Shape-based Snake Model Combined with Generalized Hough Transformation

A novel method for segmentation of cardiac structures in temporal echocardiographic sequences based on the snake model is presented. The method is motivated by the observation that the structures of neighboring frames have consistent locations and shapes that aid in segmentation. To cooperate with the constraining information provided by the neighboring frames, we combine the template matching with the conventional snake model. It means that the model not only is driven by conventional internal and external forces, but also combines an additional constraint, the matching degree to measure the similarity between the neighboring prior shape and the derived contour. Furthermore, in order to auto or semi-automatically segment the sequent images without manually drawing the initial contours in each image, generalized Hough transformation (GHT) is used to roughly estimate the initial contour by transforming the neighboring prior shape. The method is particularly useful in case of the large frame-to-frame displacement of structure such as mitral valve. As a result, the active contour can easily detect the desirable boundaries in ultrasound images and has a high penetrability through the interference of various undesirables, such as the speckle, the tissue-related textures and the artifacts.     

An adaptive snake model for ultrasound image segmentation: modified trimmed mean filter, ramp integration and adaptive weighting parameters

The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MTM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR > or = 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.     

Texture feature analysis for breast ultrasound image enhancement

Texture analysis of breast ultrasound B-scans has been widely applied to the segmentation and classification of breast tumors. We present a parametric imaging method based on the texture features to preserve tumor edges and retain the texture information simultaneously. Four texture-feature parameters--homogeneity, contrast, energy and variance--were evaluated using the gray-level co-occurrence matrix. The local texture-feature parameter was assigned as the new pixel located at the center of the sliding window at each position. This process yielded the texture-feature parametric image as the map of texture-feature values. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were estimated to show the quality improvement of the images. The contours outlined from 11 experienced physicians and the gradient vector flow (GVF) snake algorithm segmentations were adopted to verify the edge enhancement of texture-feature parametric images. In addition, the Fisher's linear discriminant analysis (FLDA) and receiver-operating-characteristic (ROC) curve were used to test the performance of breast tumor classifications between texture-feature parametric images and B-scan images. The results show that the variance images have higher CNR and SNR estimates than those in the B-scan images. There was a high agreement between the physician's manual contours and the GVF snake automatic segmentations in the variance images, and the mean area overlap was over 93%. The area under the ROC curve from the B-scan images had 0.81 and 95% confidence interval of 0.72-0.88, and the texture-feature parametric images had 0.90 and 95% confidence interval of 0.84-0.96. These findings indicate that the texture-feature parametric imaging method can be not only useful for determining the location of the lesion boundary but also as a tool to improve the accuracy of breast tumor classifications.     

Motion-guided segmentation for cine DENSE MRI

Defining myocardial contours is often the most time-consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two-dimensional semi-automated segmentation technique that uses the encoded motion to project a manually-defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, (1) parameters are based on practical physiological limits, (2) contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and (3) the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36+/-0.08 and 0.40+/-0.10 pixels for slice-followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46+/-0.12 and 0.46+/-0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.