基于改进C-V模型的超声心动图像轮廓的提取方法

目的:通过超声图像预处理和对图像分割方法的改进,完成超声心动图中心腔轮廓的提取。方法:首先,运用基于斑点指数的滤波方法对超声图像进行去噪。其次,对超声图像进行分段非线性灰度变换,提高图像对比度。最后,利用改进的基于C-V模型的水平集算法对超声图像进行分割,得到精确的初始轮廓。结果:1基于斑点指数的图像滤波方法可以在不丢失细节的情况下对超声图像进行噪声滤除。2分段非线性灰度变换可以有效提高超声图像的对比度。3改进的C-V模型可以成功的对含有斑点噪声的超声图像进行分割。结论:本文的超声图像预处理方法和分割算法可以有效提取心腔轮廓,降低斑点噪声对图像分割结果的影响。     

乳腺三维超声成像的文献计量和可视化分析*

目的:研究乳腺三维超声成像领域的发展态势并识别研究热点。方法:本研究选取PubMed数据库中乳腺三维超声成像领域的论文为研究对象,采用文献计量学方法,结合可视化分析工具,从论文数量、生命周期、国家/地区分布及合作、机构分布及合作、研究热点等多个角度对乳腺三维超声成像领域进行研究。结果:乳腺三维超声成像领域共发表论文213篇;25个国家/地区在该领域开展研究,其中美国发文量全球排名第一位;全球有100多个机构开展研究,主要以医院和大学为主;识别出研究热点有4个,分别是:乳腺肿瘤的三维弹性成像研究,乳腺X线图像的计算机辅助诊断方法研究,乳腺癌的磁共振成像诊断研究,乳腺肿瘤超声图像特征的算法研究。结论:乳腺三维超声成像领域近几年发展较快,其研究热点主要围绕乳腺癌诊断的相关研究。     

东亚飞蝗雄性生殖系统组织结构观察及三维可视化数字模型

本文研究东亚飞蝗Locusta migratoria manilensis(Meyen)雄性生殖系统组织解剖结构及三维可视化数字模型的构建。采用石蜡切片技术对东亚飞蝗进行组织切片,脱水、透明、HE染色和拍照;并应用冰冻切片技术将冰冻包埋剂包埋后的飞蝗雄性个体进行连续切片,进行截面图像信息采集,建立数据集;通过Photoshop、Image-Pro Plus(IPP)软件对雄性生殖器官截面图像进行分割、处理、序列化和三维重建。通过试验观察分析东亚飞蝗雄性生殖器官精巢、附腺、输精管、精球囊和射精囊及交配器的组织构造;并成功构建了飞蝗雄性生殖系统的三维结构可视化数字模型。该模型可以任意旋转,能从不同角度观察,该试验为研究和教学提供了理论基础。     

基于邻域最大差值与区域合并的油茶毒蛾幼虫图像分割

【目的】油茶树害虫的种类较多,其中油茶毒蛾Euproctis pseudoconspersa幼虫是危害较大的害虫之一。为完成油茶毒蛾幼虫的自动检测需要对其图像进行分割,油茶毒蛾幼虫图像的分割效果直接影响到图像的自动识别。【方法】本文提出了基于邻域最大差值与区域合并的油茶毒蛾幼虫图像分割算法,该方法主要是对相邻像素RGB的3个分量进行差值运算,最大差值若为0,则进行相邻像素合并得出初始的分割图像,根据合并准则进一步合并,得到最终分割结果。【结果】实验结果表明,该算法可以快速有效地将油茶毒蛾幼虫图像中的背景和虫体分割开来。【结论】使用JSEG分割算法、K均值聚类分割算法、快速几何可变形分割算法和本文算法对油茶毒蛾幼虫图像进行分割,将结果进行对比发现本文方法的分割效果最佳,且处理时间较短。     

不同滤波方法对超声图像分割的影响

采用各向异性滤波方法以及Gabor滤波方法对乳腺肿瘤超声图像进行处理,再使用snake方法以及levelset方法在设置相同参数的条件下,对过滤的图像分别进行分割。试验结果表明,不同的滤波方法在分害4目标图像的收敛度,边缘圆滑度及整体轮廓提取效果都有较大影响,为分割图像选取适当的滤波器提供了参考。     

磁共振肿瘤矩特征信息研究

目的:采用MR脑肿瘤图像分割与矩方法进行结合,以获取特定器官及组织的轮廓。方法:对MR脑肿瘤图像进行分割,并对分割的结果进行矩描述。通过分析当前常用的医学图像分割方法,采用了一种基于形变模型的医学图像分割方法,并按照相应的理论算法模型和实现步骤对医学图像进行了处理,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:从切割的图形中可以看出,本分割方法分割边界清晰,总体不确定性较小,利用矩技术所提取的图像特征在基于内容的图像检索中是有效的。结论:本分割方法切实可行,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。     

基于超声心动图的二尖瓣轮廓提取

针对超声心动图噪音大,灰阶少等弱点,采用多阈值的门限法对图象进行正确分割,在通过跟踪特征点进行匹配的基础上,采用匹配后插值的方法,提高了匹配的精度。并利用前一帧的速度解决了粘连在一起的二尖瓣轮廓线的分割问题。实验取得了较满意的结果。     

基于VTK的医学图像三维可视化系统

医学图像的三维可视化可以通过可视化工具包（VTK）提供的API实现。VTK是医学图像可视化的开法工具包,它把可视化的算法封装起来,利用简单的代码生成所需图形。基于VTK的医学图像三维可视化系统阐述了如何借助VTKAPI读入二维医学图像序列、操作二维图像、重建三维图像以及进行三维图像可视化的全套方案,为临床医生的诊断、治疗提供了有益的途径。     

东亚飞蝗消化道的三维重建方法研究

本文主要研究东亚飞蝗Locutsta migratoria manilansis(Meyen)消化道可视化模型三维重建方法.采用冰冻切片技术将冰冻包埋剂(OCT)包埋后的飞蝗成虫做连续切片,进行截面图像信息采集,建立数据集;通过Photoshop、Image-Pro Plus(IPP)软件对消化道截面图像进行分割、处理...     

基于主成分分析的双对比光学投影断层成像

目的 本文提出了一种基于主成分分析（PCA）的双对比光学投影断层成像（DC-OPT）方法,以获得活体中血流网络和骨骼的三维可视化。方法 使用主成分分析方法来提取吸收图像和血流图像,原始图像序列的第一主成分用于获取吸收图像;通过计算每个像素的调制深度来获得流动图像。不同投影位置的流动和吸收对比图像被用于三维血流网络和骨骼的同步重建。结果 采用PCA和OPT相结合的方法,通过将动态血流信号和静态背景信号分离,实现了对微生物样本的血流网络和骨骼的三维成像。结论 本文研究的新颖之处在于通过同一光学系统获得了快速、同步、双对比的血流网络和骨骼三维图像。实验结果可用于活体生物的生理发育研究。     

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data.     

Segmentation and detection of fluorescent 3D spots

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques.     

A visualization and segmentation toolbox for electron microscopy

Three-dimensional image analysis includes image processing, segmentation and visualization operations, which facilitate the interpretation of data. We have developed a toolbox for three-dimensional (3D) electron microscopy (EM) in Amira, which is a commercial software package, used by many laboratories. Our toolbox integrates a number of established procedures specifically tailored for 3D EM. These include input-output, filtering, segmentation, visualization and ray-tracing functions, which can be accessed directly from a user-friendly pop-up menu. They allow performing denoising and segmentation tasks directly in Amira, without the need of other programs, and ultimately allow the visualization of the results at photo-realistic quality with ray-tracing. They also allow a direct interaction with the data, such that, e.g., sub-tomograms can be directly extracted, or segmentation areas can be interactively selected. The implemented functions are fast, reliable and intuitive, yielding a comprehensive package for visualization in EM.     

Visualization and analysis of 3D microscopic images

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.     

Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network

Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm.     

Segmentation of Uterus Using Laparoscopic Ultrasound by an Image-Based Active Contour Approach for Guiding Gynecological Diagnosis and Surgery

In laparoscopic gynecologic surgery, ultrasound has been typically implemented to diagnose urological and gynecological conditions. We applied laparoscopic ultrasonography (using Esaote 7.5~10MHz laparoscopic transducer) on the retrospective analyses of 42 women subjects during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. The objective of our research is to develop robust segmentation technique for isolation and identification of the uterus from the ultrasound images, so as to assess, locate and guide in removing the lesions during laparoscopic operations. Our method enables segmentation of the uterus by the active contour algorithm. We evaluated 42 in-vivo laparoscopic images acquired from the 42 patients (age 39.1 ± 7.2 years old) and selected images pertaining to 4 cases of congenital uterine malformations and 2 cases of pelvic adhesions masses. These cases (n = 6) were used for our uterus segmentation experiments. Based on them, the active contour method was compared with the manual segmentation method by a medical expert using linear regression and the Bland-Altman analysis (used to measure the correlation and the agreement). Then, the Dice and Jaccard indices are computed for measuring the similarity of uterus segmented between computational and manual methods. Good correlation was achieved whereby 84%–92% results fall within the 95% confidence interval in the Student t-test) and we demonstrate that the proposed segmentation method of uterus using laparoscopic images is effective.     

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.     

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images.The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models.This new function is a combination of the gray-level information and first-order statistical features,called standard deviation parameters.In a comprehensive study,the developed algorithm and the efficiency of segmentation were first tested for synthetic images.Tests were also performed on breast and liver ultrasound images.The proposed method was compared with the watershed approach to show its efficiency.The performance of the segmentation was estimated using the area error rate.Using the standard deviation textural feature and a 5×5 kernel,our curve evolution was able to produce results close to the minimal area error rate(namely 8.88% for breast images and 10.82% for liver images).The image resolution was evaluated using the contrast-to-gradient method.The experiments showed promising segmentation results.