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通过高斯滤波器滤出高斯曲面作为阈值分割dicom医学图像-through Gaussian filter out the Gaussian surfaces as a threshold of medical image segmentation dicom
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类间方差阈值算法十分常见,本算法优势在于应用于分割DICOM医学图像-Class variance threshold very common algorithm, the algorithm is applied to segmentation advantage DICOM medical images
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自己根据DICOM 协议写的dicom文件读取程序，程序包括DICOM文件读取，图像分割，三维重建，边缘检测的功能 ,Their written agreement in accordance with DICOM file read dicom procedures, including procedures to read DICOM files, image segmentation, three-dimensional reconstruction, edge detection fun
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在VC++6.0集成开发环境下编程实现了MITK在医学图像分割、配准和三维可视化(体绘制).利用MITK读取五种图片格式：PEG,DICOM,BMP,TIFF.RAW。-In VC++6.0 integrated development environment programming the MITK in medical image segmentation, registration and three-dimensional visualization (volume rendering)
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Simple threshold segmentation of medical CT data in DICOM data representation.
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1、dicom .dcm文件转bmp文件
2、bmp文件转dicom文件
3、dicom头文件读取、显示
4、点运算、直方图、形态学变换、图像分割、图像编码等图像处理-1, dicom. Dcm file transfer bmp file 2, bmp file transfer dicom file 3, dicom header file to read, display 4, point operations, histogram, morphological transfo
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DICOM显示并进行分割等处理的程序，对大家学习很有帮助-DICOM display and segmentation and other processing procedures, very helpful to everyone
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itk 区域生长发分割 vtk显示 医学图像分割 dicom格式-Regional growth and development division itk vtk format display dicom medical image segmentation
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在VC++6.0集成开发环境下编程实现了MITK在医学图像分割、配准和三维可视化(体绘制).利用MITK读取五种图片格式：PEG,DICOM,BMP,TIFF.RAW。-In VC++6.0 integrated development environment programming the MITK in medical image segmentation, registration and three-dimensional visualization (volume rendering)
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用于左心室分割的Ostu法和区域增长法选取特定的分割位置，有详细标注，可用于学习与应用，附有两张BMP格式的左心室图像和3张DICOM格式的左心室图像-Left ventricular image for the left ventricle segmentation Ostu method and region growing segmentation method to specific location, detailed labels, can be used for learning
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程序功能：
1、对原始dicom病例进行中值滤波（滤波模板为3x3）
2、进行阈值分割及筛除步骤
3、导入肝掩膜凸壳，并对其腐蚀（腐蚀结构元素半径为10）
4、对2步骤中结果与肝掩膜做与运算- Program features: 1 of the original cases were dicom median filter (filter template for 3x3) 2, threshold segmentation and screening Step
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