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Know-GRRF: Domain-Knowledge Informed Biomarker Discovery with Random Forests Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI

Authors : Mohamed Amine Larhmam, Saïd Mahmoudi, Stylianos Drisis, Mohammed Benjelloun

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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Abstract

This paper presents a learning based approach for the classification of pathological vertebrae. The proposed method is applied to spine metastasis, a malignant tumor that develops inside bones and requires a rapid diagnosis for an effective treatment monitoring. We used multiple texture analysis techniques to extract useful features from two co-registered MR images sequences (T1, T2). These MRIs are part of a diagnostic protocol for vertebral metastases follow up. We adopted a slice by slice MRI analysis of 153 vertebra region of interest. Our method achieved a classification accuracy of \(90.17\% \pm 5.49\), using only a subset of 67 relevant selected features from the initial 142.

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previous chapter Quantitative Ultrasound of Tumor Surrounding Tissue for Enhancement of Breast Cancer Diagnosis
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Footnotes
1
Multidisciplinary and autonomous hospital totally dedicated to cancer located in Brussels, Belgium.
 
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Metadata
Title
A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI
Authors
Mohamed Amine Larhmam
Saïd Mahmoudi
Stylianos Drisis
Mohammed Benjelloun
Copyright Year
2018
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78758-9

Electronic ISBN: 978-3-319-78759-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78759-6_19

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