Automatic segmentation of Multiple Sclerosis Lesion in MRI using Gaussian Mixture Model

  • 鄭 綉瑩

Student thesis: Master's Thesis

Abstract

Manual lesion segmentation of Multiple Sclerosis (MS) lesions is time consuming and has an intra- and interrater variability To resolve these issues there are several auto-segmentation methods This thesis implements an automatic MS lesion segmentation method targeting white matter lesions (WML) uses three different MRI sequences (T1-w T2-w FLAIR image) as the input of our algorithm We use Gaussian Mixture Model (GMM) to estimate the parameters of Gaussian distribution of NABT (normal appearing brain tissue) and we will remove the outlier candidate data during this process to ensure correctness MS WML has hyper-intensity on T2-w and FLAIR images so we find the hyper-intensity pixels on these two kinds of images using the outlier information estimate from the previous step In the post-processing we remove false positive using rule-based methods and evaluate the volumes of lesions to help doctors supervise the progress of lesion in long term
Date of Award2018 Mar 27
Original languageEnglish
SupervisorMing-Long Wu (Supervisor)

Cite this

Automatic segmentation of Multiple Sclerosis Lesion in MRI using Gaussian Mixture Model
綉瑩, 鄭. (Author). 2018 Mar 27

Student thesis: Master's Thesis