Chapter title |
Quantitative MR Image Analysis for Brian Tumor
|
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Chapter number | 2 |
Book title |
VipIMAGE 2017
|
Published in |
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), January 2018
|
DOI | 10.1007/978-3-319-68195-5_2 |
Pubmed ID | |
Book ISBNs |
978-3-31-968194-8, 978-3-31-968195-5
|
Authors |
Zeina A. Shboul, Sayed M. S. Reza, Khan M. Iftekharuddin, Shboul, Zeina A., Reza, Sayed M. S., Iftekharuddin, Khan M. |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Lecturer | 1 | 50% |
Unknown | 1 | 50% |
Readers by discipline | Count | As % |
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Engineering | 1 | 50% |
Unknown | 1 | 50% |