Medical image segmentation is a classical puzzle for researchers.
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 traditional and challenging research point.
医学图像分割是一个传统而具有挑战性的课题。
Medical image segmentation is an important application of image segmentation.
图像分割是制约医学图像在临床上广泛应用的关键性问题。
Microscopic medical image segmentation is a problem in medical image process.
医学显微图像分割是医学图像处理中的一个经典难题。
Objective to improve the automatization and reliability of medical image segmentation.
目的提高图像分割技术的自动化程度和可靠性。
A semiautomatic method for medical image segmentation based on active shape model was introduced.
较详细地介绍了一种图像轮廓的半自动提取算法：活动轮廓模型的原理与实现。
Also, Medical image segmentation is the foundation and difficulty in image processing and analysis.
医学图像分割，也是医学图像处理和分析的基础与难题。
Medical image segmentation plays a key role in medical diagnosis, planning and clinical applications.
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。
Results of experiments show that watershed transformation presents a new method 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。
In this paper, the clinical applications of medical image segmentation techniques in vasculature imaging were reviewed.
本文综述图像分割技术在血管图像方面的应用。
In this paper, a level set segmentation algorithm based on Bayesian classification for medical image segmentation was proposed.
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, the theoretical knowledge and practical methods on medical image segmentation and feature extraction are studied and analyzed.
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。
Medical image segmentation is an important field in image processing and analysis, and it's the basis for computer assisted diagnosis and clinical treatment.
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。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
Objective evaluation of medical image segmentation algorithms is one of the important steps toward establishing validity and clinical applicability of an algorithm.
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。
Then a new method merging area information and edge information, geometric deformable model with color and intensity priors for medical image segmentation, is proposed.
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。
After analyzing snake and geometric active contour model, a minimum variation snake model was proposed and successfully applied to weak edge medical image segmentation.
我们在分析参数活动轮廓和几何活动轮廓模型的基础上，提出最小方差参数活动轮廓模型，并成功应用于医学图像自动分割。
For the medical image segmentation, a good accuracy of results is very important and helpful for doctors to diagnose the illness and make the right therapeutic schemes.
对于医学图像而言，其分割结果的准确性对医生诊断病情并做出正确的治疗方案至关重要。
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。
The dissertation first introduces the background of medical image segmentation, MRI imaging mechanism, the segmentation target, and the assessment rules for segmentation results.
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。
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.
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。
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.
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。
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。
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。
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。
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.
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。
Aiming at the task of the organ modeling, this thesis focuses on the techniques of organ contour-detecting, segmentation and modeling based on medical image.
本文针对人体器官重建这一研究课题，对基于医学影像的器官轮廓检测、分割和模型重建等技术进行了深入的研究。
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 one of the basic technologies in remote sensing and medical image processing but it lacks current algorithm for engineering applications.
图像分割是遥感和医学图像处理的基础技术之一，但是目前缺乏工程化通用算法。
Image segmentation is one of the basic technologies in remote sensing and medical image processing but it lacks current algorithm for engineering applications.
图像分割是遥感和医学图像处理的基础技术之一，但是目前缺乏工程化通用算法。
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