医学图像分割是一个传统而具有挑战性的课题。
Medical image segmentation is a traditional and challenging research point.
医学图像分割是医学图像处理中的一个经典难题。
Medical image segmentation is a classical puzzle for researchers.
医学图像分割，也是医学图像处理和分析的基础与难题。
Also, Medical image segmentation is the foundation and difficulty in image processing and analysis.
此外，还介绍了如何建立医学图像分割金标准数据库的方法。
Moreover, the author also presents a method on how to construct gold standard of medical images.
因此，对医学图像分割及三维可视化的研究具有重要的意义。
So research on Segmentation and 3d visualization of medical images has deep significance.
本文提出了一种结合贝叶斯分类的水平集方法用于医学图像分割。
In this paper, a level set segmentation algorithm based on Bayesian classification for medical image segmentation was proposed.
我们提出了等高地图分割法用于激光共焦显微生物医学图像分割。
We propose a contour map segmentation method for laser scanning confocal microscopic (LSCM) biomedical images.
医学图像分割在医学诊断、规划、治疗中具有十分重要的应用价值。
Medical image segmentation plays a key role in medical diagnosis, planning and clinical applications.
对医学图像分割算法的客观评价是推进算法在临床上得到应用的关键。
Objective evaluation of medical image segmentation algorithms is one of the important steps toward establishing validity and clinical applicability of an algorithm.
为获得更精确的重建结果，提出了一种改进的交互式医学图像分割算法；
To obtain the more accurate results, an interactive image segmentation algorithm is presented.
本文研究并分析了医学图像分割和医学图像特征提取的相关理论与方法。
In this paper, the theoretical knowledge and practical methods on medical image segmentation and feature extraction are studied and analyzed.
医学图像分割是医学图像处理与分析的一个重要领域，同时也是计算机辅助诊断与治疗的基础。
Medical image segmentation is an important field in image processing and analysis, and it's the basis for computer assisted diagnosis and clinical treatment.
文章首先介绍了医学图像分割的相关背景、MRI成像机理和分割目标，以及分割结果的评估方法。
The dissertation first introduces the background of medical image segmentation, MRI imaging mechanism, the segmentation target, and the assessment rules for segmentation results.
医学图像分割技术是医学图像处理与分析领域的重要课题之一，也是近年来备受研究人员关注的热点问题。
In recent years medical image segmentation technology is one of the important subjects in medical image processing and analysis research field, and has been a hot issue for researchers.
医学图像分割在医学影像分析中正在发挥着日益重要的作用，它是医学图像处理和分析领域的基础性经典难题。
Medical image segmentation has been playing an increasingly important role in medical image analysis; it is a hard-tough problem in medical image processing and analysis.
医学图像分割在医学解剖结构的研究，诊断等起着至关重要的作用，因此对医学图像分割算法的研究非常必要。
The segmentation of medical image applying in medical anatomy plays an important role in diagnosis. So the study of medical image arithmetic is very urgent and necessary.
医学图像分割是图像分割领域的一个经典问题，由于医学图像的复杂性，到目前为止还不存在一个通用的分割方法。
Medical image segmentation is a classic problem in image segmentation field, because of the complexity of medical images, so far there is not any all-purpose segmentation method.
超声医学图像分割是对超声图像进行分析的基本步骤，也是利用超声图像进行定性、定量分析的一个至关重要的环节。
Ultrasound medical image segmentation is the essential step of ultrasound image processing, and it plays a crucial role in both qualitative and quantitative ultrasound image analyses.
实验结果表明，相比与GVF方法，IGVF方法对边界复杂且具有又深又细凹口的医学图像分割效果良好，且迭代次数少。
The results of the experiments show that, compared to GVF, IGVF can achieve better results with less number of iterations for images with long and thin boundary concavities.
在此基础上提出了一种混合区域信息和边界信息的方法——基于融合颜色和强度先验信息的几何可变模型的医学图像分割算法。
Then a new method merging area information and edge information, geometric deformable model with color and intensity priors for medical image segmentation, is proposed.
实验结果中的欠分割率、过分割率和错误分割率表明DCMIS比DENCLUE和FCM算法有更好的性能和较好的医学图像分割效能。
Experiment results of over-segmentation, under-segmentation and incorrect segmentation rates show that DCMIS has better validity and correctness than DENCLUE and FCM for medical image segmentation.
实验结果表明，流域变换为医学图像提供了一种崭新的分割方法。
Results of experiments show that watershed transformation presents a new method for medical image segmentation.
图像分割在计算机视觉、图像编码、模式识别、医学图像分析等很多领域有着实际的应用。
Image segmentation is applied in a lot of fields such as computer vision, image coding, pattern recognition, medical image and so on.
图像分割在医学超声图像的定量、定性分析中均扮演着重要的角色，它直接影响到后续的分析、处理工作。
Picture partition plays an important role in both qualitative and quantitative analysis of medical ultrasonic images and it directly influences on the subsequent analysis and treatment.
本文主要研究了医学X光胸片中的几个关键图像处理技术，主要包括X光胸片图像增强、分割和肺部病灶识别。
The paper focuses on the research of some key medical image processing technologies in chest X-rays, including chest Radiography images enhancement, segmentation and focus recognition of lung.
针对医学图像的模糊特点和实际应用的要求，提出了一种基于动态合并准则的分水岭分割方法。
In view of the fuzziness of medical image and the requirement in practical application, a watershed segmentation method was proposed based on the dynamic combination rule.
目的解决医学图象多维重建中最困难的问题之一：图像分割问题。
Objective To solve one of the most difficult problems in multi dimensional reconstruction of medical ultrasonic images: image segmentation.
提出了一种医学CT图像分割的新方法。
A novel method for medical CT image segmentation is proposed in this paper.
图像分割是很多高级图像处理技术(如可视化、图像压缩、医学图像诊断等)的重要基础工作。
Image segmentation is a key basis of many higher level image processing activities such as visualization, compression, and image guided medical diagnoses.
图像分割是很多高级图像处理技术(如可视化、图像压缩、医学图像诊断等)的重要基础工作。
Image segmentation is a key basis of many higher level image processing activities such as visualization, compression, and image guided medical diagnoses.
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