02028nas a2200169   4500008004100000245011800041210006900159260004200228300001600270520130800286100001601594700002201610700002801632700002701660700001701687856015401704       2004                        eng d00aMultiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness0 aMultiple Sclerosis lesion quantification in MR images by using v  aBellingham; WashingtonbSPIEc2004///  a1764 - 17733 aThis paper presents a methodology for segmenting PD- andT2-weighted brain magnetic resonance (MR) images of 
multiplesclerosis (MS) patients into white matter (WM), gray 
matter (GM),cerebrospinal fluid (CSF), and MS lesions. For a 
given vectorialimage (with PD- and T2-weighted components) to be 
segmented, weperform first intensity inhomogeneity correction 
andstandardization prior to segmentation. Absolute 
fuzzyconnectedness and certain morphological operations are 
utilized togenerate the brain intracranial mask. The optimum 
thresholdingmethod is applied to the product image (the image in 
which voxelvalues represent T2 value x PD value) to 
automaticallyrecognize potential MS lesion sites. Then, the 
recently developedtechnique -- vectorial scale-based relative 
fuzzy connectedness --is utilized to segment all voxels within 
the brain intracranialmask into WM, GM, CSF, and MS lesion 
regions. The number ofsegmented lesions and the volume of each 
lesion are finally outputas well as the volume of other tissue 
regions. The method has beentested on 10 clinical brain MRI data 
sets of MS patients. Anaccuracy of better than 96% has been 
achieved. The preliminaryresults indicate that its performance 
is better than that of thek-nearest neighbors (kNN) method.
1 aZhuge, Ying1 aUdupa, Jayaram, K1 aNyúl, László, Gábor1 aFitzpatrick, J Michael1 aSonka, Milan  uhttps://inf.u-szeged.hu/publication/multiple-sclerosis-lesion-quantification-in-mr-images-by-using-vectorial-scale-based-relative-fuzzy-connectedness