Multiple sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness

Ying Zhuge; Jayaram K. Udupa; Laszlo G. Nyul

doi:10.1117/12.535655

12 May 2004 Multiple sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness

Ying Zhuge, Jayaram K. Udupa, Laszlo G. Nyul

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535655
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

This paper presents a methodology for segmenting PD- and T2-weighted brain magnetic resonance (MR) images of multiple sclerosis (MS) patients into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and MS lesions. For a given vectorial image (with PD- and T2-weighted components) to be segmented, we perform first intensity inhomogeneity correction and standardization prior to segmentation. Absolute fuzzy connectedness and certain morphological operations are utilized to generate the brain intracranial mask. The optimum thresholding method is applied to the product image (the image in which voxel values represent T2 value x PD value) to automatically recognize potential MS lesion sites. Then, the recently developed technique -- vectorial scale-based relative fuzzy connectedness -- is utilized to segment all voxels within the brain intracranial mask into WM, GM, CSF, and MS lesion regions. The number of segmented lesions and the volume of each lesion are finally output as well as the volume of other tissue regions. The method has been tested on 10 clinical brain MRI data sets of MS patients. An accuracy of better than 96% has been achieved. The preliminary results indicate that its performance is better than that of the k-nearest neighbors (kNN) method.

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Ying Zhuge, Jayaram K. Udupa, and Laszlo G. Nyul "Multiple sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535655

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