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Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

Identifieur interne : 003B30 ( PubMed/Corpus ); précédent : 003B29; suivant : 003B31

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

Auteurs : Jean-Baptiste Fiot ; Laurent D. Cohen ; Parnesh Raniga ; Jurgen Fripp

Source :

International journal for numerical methods in biomedical engineering [ 2040-7947 ] ; 2013.

RBID : pubmed:23303595

English descriptors

KwdEn :
- Brain (anatomy & histology), Brain (pathology), Brain Neoplasms (pathology), Humans, Image Processing, Computer-Assisted (methods), Magnetic Resonance Imaging, Neuroimaging (methods), Reproducibility of Results, Support Vector Machine.
MESH :
- anatomy & histology : Brain.
- methods : Image Processing, Computer-Assisted, Neuroimaging.
- pathology : Brain, Brain Neoplasms.
- Humans, Magnetic Resonance Imaging, Reproducibility of Results, Support Vector Machine.

Abstract

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54 ± 0.12, 0.72 ± 0.06 and 0.82 ± 0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52 ± 0.13, 0.71 ± 0.08 and 0.81 ± 0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing.

DOI: 10.1002/cnm.2537
PubMed: 23303595

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pubmed:23303595

Le document en format XML

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<author><name sortKey="Cohen, Laurent D" sort="Cohen, Laurent D" uniqKey="Cohen L" first="Laurent D" last="Cohen">Laurent D. Cohen</name>
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<author><name sortKey="Raniga, Parnesh" sort="Raniga, Parnesh" uniqKey="Raniga P" first="Parnesh" last="Raniga">Parnesh Raniga</name>
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<front><div type="abstract" xml:lang="en">Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing.</div>
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Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

Source :

English descriptors

Abstract

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