文件名称:ImageRough_FullPackage
- 所属分类：
- 数学计算/工程计算
- 资源属性：
- [C/C++] [源码]
- 上传时间：
- 2016-09-01
- 文件大小：
- 14.59mb
- 下载次数：
- 0次
- 提 供 者：
- Mohamed A**********
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
we peresent full pakage of the methods that apply rough set theory in the
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic fr a mework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach.-we peresent full pakage of the methods that apply rough set theory in the
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic fr a mework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach.
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic fr a mework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach.-we peresent full pakage of the methods that apply rough set theory in the
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic fr a mework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach.
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下载文件列表
ImageRough_FullPackage\CC.ppm
......................\.lasses\Class0.png
......................\.......\Class1.png
......................\.......\Class10.png
......................\.......\Class11.png
......................\.......\Class12.png
......................\.......\Class13.png
......................\.......\Class14.png
......................\.......\Class15.png
......................\.......\Class2.png
......................\.......\Class3.png
......................\.......\Class4.png
......................\.......\Class5.png
......................\.......\Class6.png
......................\.......\Class7.png
......................\.......\Class8.png
......................\.......\Class9.png
......................\.......\kmeans.png
......................\.......\New Folder\Class0.png
......................\.......\..........\Class1.png
......................\.......\..........\Class10.png
......................\.......\..........\Class11.png
......................\.......\..........\Class12.png
......................\.......\..........\Class13.png
......................\.......\..........\Class14.png
......................\.......\..........\Class15.png
......................\.......\..........\Class2.png
......................\.......\..........\Class3.png
......................\.......\..........\Class4.png
......................\.......\..........\Class5.png
......................\.......\..........\Class6.png
......................\.......\..........\Class7.png
......................\.......\..........\Class8.png
......................\.......\..........\Class9.png
......................\.......\..........\kmeans.png
......................\.......\..........\rough.png
......................\.......\ppm\Class5.ppm
......................\.......\...\Class6.ppm
......................\.......\rough.png
......................\iconFile.qrc
......................\.mages\0-classify.png
......................\......\addtab.png
......................\......\classify.png
......................\......\clear.png
......................\......\Clear_Text.png
......................\......\closetab.png
......................\......\copy.png
......................\......\cut.png
......................\......\editcopy.png
......................\......\find.png
......................\......\home.png
......................\......\input.png
......................\......\new.png
......................\......\next.png
......................\......\open.png
......................\......\openRaw.png
......................\......\paste.png
......................\......\previous.png
......................\......\print.png
......................\......\resetzoom.png
......................\......\Save.png
......................\......\select.png
......................\......\slect.png
......................\......\synctoc.png
......................\......\undo.png
......................\......\zoomin.png
......................\......\zoomout.png
......................\image_klassifier
......................\image_klassifier.pro
......................\image_klassifier.pro.user
......................\input0.raw
......................\K-Means_data.txt
......................\L.txt
......................\main.cpp
......................\main.o
......................\mainwindow.cpp
......................\mainwindow.h
......................\mainwindow.o
......................\mainwindow.ui
......................\Makefile
......................\moc_mainwindow.cpp
......................\moc_mainwindow.o
......................\noise100A.ppm
......................\qrc_iconFile.cpp
......................\qrc_iconFile.o
......................\Rough_data.txt
......................\Rough_data.txt~
......................\Sample.ppm
......................\t2n100.ppm
......................\t2n60.ppm
......................\t2n80.ppm
......................\tt.ppm
......................\U.txt
......................\U.txt~
......................\ui_mainwindow.h
......................\Classes\New Folder
......................\.......\ppm
....................