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.