Title:	INTENSITY NORMALIZATION IN BRAIN MR IMAGES USING SPATIALLY VARYING DISTRIBUTION MATCHING
Author(s):	Evgin Goceri
ISBN:	978-989-8533-66-1
Editors:	Yingcai Xiao and Ajith P. Abraham
Year:	2017
Edition:	Single
Keywords:	Intensity normalization, bias field correction, intensity standardization, inhomogeneity correction, spatial transformation
Type:	Short Paper
First Page:	300
Last Page:	304
Language:	English
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Paper Abstract:	Comparison of medical images is frequently needed for diagnosis or evaluation of a progressive disease. The comparison is performed by alignment and registration of images or warping them with a transformation function. It is possible to compare images of the same patient, which have been taken at different time periods to detect or quantify the changes that might have taken place in-between acquisitions. Also, a comparison can be performed using images from different subjects. In a Magnetic Resonance Image (MRI), intensity values do not only depend on the underlying tissue type. They also depend on developmental processes, scanner-related intensity artifacts and disease progression. Therefore, spatial normalization, which brings an image into the coordinate system of a template using a coordinate transformation to make meaningful comparisons of spatially varying data, is required. In this work, an intensity normalization method based on spatially varying distribution matching is proposed. The efficiency of the proposed method has been shown on brain MRIs.
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