Brain tumor segmentation with Vander Lugt correlator based active contour.
Identifieur interne : 000459 ( Main/Exploration ); précédent : 000458; suivant : 000460Brain tumor segmentation with Vander Lugt correlator based active contour.
Auteurs : Abdelaziz Essadike [Maroc] ; Elhoussaine Ouabida [Maroc] ; Abdenbi Bouzid [Maroc]Source :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2018.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Bases de données factuelles (statistiques et données numériques), Fantômes en imagerie (MeSH), Humains (MeSH), Imagerie par résonance magnétique (méthodes), Imagerie par résonance magnétique (statistiques et données numériques), Interprétation d'images assistée par ordinateur (méthodes), Simulation numérique (MeSH), Tumeurs du cerveau (anatomopathologie), Tumeurs du cerveau (imagerie diagnostique).
- MESH :
- anatomopathologie : Tumeurs du cerveau.
- imagerie diagnostique : Tumeurs du cerveau.
- méthodes : Imagerie par résonance magnétique, Interprétation d'images assistée par ordinateur.
- statistiques et données numériques : Bases de données factuelles, Imagerie par résonance magnétique.
- Algorithmes, Fantômes en imagerie, Humains, Simulation numérique.
English descriptors
- KwdEn :
- Algorithms (MeSH), Brain Neoplasms (diagnostic imaging), Brain Neoplasms (pathology), Computer Simulation (MeSH), Databases, Factual (statistics & numerical data), Humans (MeSH), Image Interpretation, Computer-Assisted (methods), Magnetic Resonance Imaging (methods), Magnetic Resonance Imaging (statistics & numerical data), Phantoms, Imaging (MeSH).
- MESH :
- diagnostic imaging : Brain Neoplasms.
- methods : Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging.
- pathology : Brain Neoplasms.
- statistics & numerical data : Databases, Factual, Magnetic Resonance Imaging.
- Algorithms, Computer Simulation, Humans, Phantoms, Imaging.
Abstract
BACKGROUND AND OBJECTIVE
The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation.
METHODS
In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages.
RESULTS
The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image.
CONCLUSIONS
Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation.
DOI: 10.1016/j.cmpb.2018.04.004
PubMed: 29728237
Affiliations:
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Le document en format XML
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<term>Computer Simulation (MeSH)</term>
<term>Databases, Factual (statistics & numerical data)</term>
<term>Humans (MeSH)</term>
<term>Image Interpretation, Computer-Assisted (methods)</term>
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<term>Bases de données factuelles (statistiques et données numériques)</term>
<term>Fantômes en imagerie (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Imagerie par résonance magnétique (méthodes)</term>
<term>Imagerie par résonance magnétique (statistiques et données numériques)</term>
<term>Interprétation d'images assistée par ordinateur (méthodes)</term>
<term>Simulation numérique (MeSH)</term>
<term>Tumeurs du cerveau (anatomopathologie)</term>
<term>Tumeurs du cerveau (imagerie diagnostique)</term>
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<term>Interprétation d'images assistée par ordinateur</term>
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<term>Magnetic Resonance Imaging</term>
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<term>Humans</term>
<term>Phantoms, Imaging</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND AND OBJECTIVE</b>
</p>
<p>The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation.</p>
</div>
</front>
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