文件名称:fuzzycmeans
介绍说明－－下载内容均来自于网络，请自行研究使用
Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS)
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.-Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS)
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.-Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS)
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
fuzzycmeans\fuzzycmeans.m
...........\license.txt
fuzzycmeans