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live-wire是一种常用于医学图像的交互式分割方法,其基本思想是利用动态规划方法产生图像中给定两点间的最优路径,合理地构造代价函数和选择起始点和目标点,用以提取物体的边缘,十分有效,该份文件中包含说明文件,操作方便.-live-wire is a common medical images in the interactive segmentation method, The basic idea is to use dynamic programming methods to image
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mitk医学影像图像处理软件，特别是针对CT进行分割提取，得出病灶。,mitk medical imaging software, image processing, especially for CT segmentation extract, derived lesions.
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用于检测3D血管图像的分叉点的一个程序，文件夹内还有说明文档，详细介绍了该程序的算法。是用itk库做的一个通用模板类，还有例子。- Branches of tubular structures (vasculature, trachea, neuron, etc.) in medical images are critical for the topology of these
structures. In many applications,
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本书全面系统介绍了小波分析的基本理论和最新研究成果，重点介绍小波分析的应用成果，并通过软件实现来检验应用效果。全书分为三篇：第一篇是小波理论，包含8章内容，小波分析的发展历史及文献综述、准备知识、多分辨分析与共轭滤波器、连续小波变换、最佳小波基的构造及算法、二维母小波的构造、框架与样条小波理论、时间----频率分析；第二篇是小波应用，包含12章内容，详细介绍了小波分析在图象压缩、流体力学、工业CT、故障诊断、语音分割、数学物理、地球物理勘探、医学细胞识别、线性系统、神经网络等方面的应用；第三篇是
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数字图像处理(DigitalImageProcessing)是通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。数字图像处理的产生和迅速发展主要受三个因素的影响:一是计算机的发展 二是数学的发展(特别是离散数学理论的创立和完善) 三是广泛的农牧业、林业、环境、军事、工业和医学等方面的应用需求的增长。-Digital Image Processing (DigitalImageProcessing) is the image by computer noise remov
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This chapter gives an introduction to the methods of biomedical image
processing. After some fundamental preliminary remarks to the terminology used,
medical imaging modalities are introduced (Sect. 1.2). Sections 1.3 and 1.4 deal
with low-leve
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数字图像处理(DigitalImageProcessing)是通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。数字图像处理的产生和迅速发展主要受三个因素的影响：一是计算机的发展；二是数学的发展（特别是离散数学理论的创立和完善） 三是广泛的农牧业、林业、环境、军事、工业和医学等方面的应用需求的增长。
MATLAB和Mathematica、Maple并称为三大数学软件。它可以进行矩阵运算、绘制函数和数据、实现算法、创建用户界面、连接其他编程语言的程序等，主要应用于工程
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Segmentation of structure in medical images is an important research
topic. It has applications in patient diagnoses, image-guided
surgery, and medical data visualization. One approach to solving this
problem is the use of active contours or \s
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Segmentation of structure in medical images is an important research
topic. It has applications in patient diagnoses, image-guided
surgery, and medical data visualization. One approach to solving this
problem is the use of active contours or \s
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Segmentation of structure in medical images is an important research
topic. It has applications in patient diagnoses, image-guided
surgery, and medical data visualization. One approach to solving this
problem is the use of active contours or \s
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Segmentation of structure in medical images is an important research
topic. It has applications in patient diagnoses, image-guided
surgery, and medical data visualization. One approach to solving this
problem is the use of active contours or \s
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The segmentation of structure from 2D and 3D images is an important rst
step in analyzing medical data. For example, it is necessary to segment the
brain in an MR image, before it can be rendered in 3D for visualization
purposes. Segmentation
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The segmentation of structure from 2D and 3D images is an important rst
step in analyzing medical data. For example, it is necessary to segment the
brain in an MR image, before it can be rendered in 3D for visualization
purposes. Segmentation
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The segmentation of structure from 2D and 3D images is an important rst
step in analyzing medical data. For example, it is necessary to segment the
brain in an MR image, before it can be rendered in 3D for visualization
purposes. Segmentation
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The segmentation of structure from 2D and 3D images is an important rst
step in analyzing medical data. For example, it is necessary to segment the
brain in an MR image, before it can be rendered in 3D for visualization
purposes. Segmentation
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this is good performance code for image segmentation code for medical image search engine which is used to beginners image processing
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by traditional level set based segmentation models. In this paper, we propose a new level set
method integrating local and global intensity information adaptively to segment inhomogeneous images. The
local image information is associated with the
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该MATLAB程序主要用来实现医学CT图像的处理，包括图像滤波、扫描、腐蚀、膨胀等算法。-The MATLAB program is mainly used in medical CT image processing, including image filtering, scanning, erosion and dilation algorithms.
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Melanoma is one of the deadly diseases among skin
cancer. Melanoma detection can be done by dermatological
screening and biopsy tests which are time consuming and
expensive that requires experts from medical field. Due to cost of
dermatologist to
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UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FP
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