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Quantitative MR Image Analysis for Brian Tumor.


ABSTRACT: This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.

SUBMITTER: Shboul ZA 

PROVIDER: S-EPMC5719874 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Quantitative MR Image Analysis for Brian Tumor.

Shboul Zeina A ZA   Reza Sayed M S SMS   Iftekharuddin Khan M KM  

VipIMAGE 2017 : proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017. VipIMAGE (Conference) (2017 : Porto, Portugal) 20171013


This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD)  ...[more]

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