搜索资源列表
Deep-ADMM-Net-master
- Net is defined over a data flow graph, which is derived from the iterative pro- cedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a CS-based MRI model. In the training phase, all parameters of the net, e.g., im
BraTS2017特征提取
- 提取标注过的MRI影像的特征，每个点可展开为237维向量。(The features of the labeled MRI image are extracted, and each point can be expanded to a 237 dimensional vector.)
GABP
- 聚类实现的另外一种算法，效果OK，可以参考使用，脑部分组织分割不错。(Another algorithm implemented by clustering is OK, which can be used for reference. Partial segmentation of brain tissue is good.)
NIFTI
- 这个Matlab程序包用于处理核磁共振图像，可以读取显示、保存、制作该图像。(The Matlab package is used to process MRI images, and can read, display, save and produce the image.)
ADMM_QSM
- matlab用来处理磁共振QSM数据的小程序包(A matlab package for processing magnetic resonance QSM data)
3D脑部MRI分割
- 图像分割mr灰白质，脑腔，好几个文件，要在matlab下都运行，先运行serve那个。挺好用的(Image segmentation Mr Gray matter, brain cavity, several files, to run in matlab, run that first. Good use of)
Brain_Tumor
- 利用matlab对脑部肿瘤区域进行识别和分割(Recognition and segmentation of brain tumors using MATLAB)
fsl_preanalyze
- fsl专用于处理脑影像fMRI或MRI数据，此脚本用于MRI或fMRI数据的预处理，需要结合自己的实验设计进行适当调整(FSL is specially designed for processing fMRI or MRI data of brain images. This scr ipt is used for preprocessing of MRI or fMRI data. It needs to be adjusted properly according to its own e
MRI_Brain_Scan
- 使用SVM和聚类分割的方式实现MRI图像中的脑瘤分割(Brain Tumor Segmentation in MRI Images Using SVM and Cluster Segmentation)
sparseMRI_v0.2.tar
- 代码是关于压缩感知磁共振成像的具体实现，运行环境为MATLAB，十分经典的代码(The code is about the realization of compressed perceptual MRI. The running environment is matlab, which is a very classic code)
CS_OMP
- 是关于压缩感知磁共振成像的正交匹配追踪算法的具体实现，用于稀疏磁共振成像，可以进行仿真实验(The code is about the realization of compressed perceptual MRI. The running environment is matlab, which is a very classic code)
Medical-Imaging-Tutorial-master
- 这是一个ＭＲＩ重建的实例教程。教程中完成了ＳＥＮＳＥ、ＧＲＡＰＰＡ等并行磁共振重建。(This is an example tutorial of MRI reconstruction. In this course, parallel magnetic resonance reconstructions such as SENSE and GRAPPA were completed.)
Python极客项目编程
- 《Python极客项目编程》里面讲解了一些很好玩的项目。 利用参数方程和turtle模块生成万花尺图案； ● 通过模拟频率泛音在计算机上创作音乐； ● 将图形图像转换为ASCII文本图形； ● 编写一个三维立体画程序，生成隐藏在随机图案下的3D图像； ● 通过探索粒子系统、透明度和广告牌技术，利用OpenGL着色器制作逼真的动画； ● 利用来自CT和MRI扫描的数据实现3D可视化； ● 将计算机连接到Arduino编程，创建响应音乐的激光秀。(Python Geek Program
三维重建
- 此文档是基于mri的颅脑三维重建，内容清楚记录了实现过程