Titre :	Segmentation d’images de résonance magnétique de diffusion pour l’aide au diagnostic des pathologies cérébrales fœtales.
Type de document :	texte imprimé
Auteurs :	Mohamed Zaki Abderrezak, Auteur ; Karim Mansour, Directeur de thèse
Editeur :	جامعة الإخوة منتوري قسنطينة
Année de publication :	2018
Format :	30 cm.
Note générale :	2 copies imprimées disponibles
Langues :	Français (fre)
Catégories :	Français - Anglais Electronique
Tags :	IRM fœtale  Segmentation  Artefacts  Contour Active Géodésique  Gradient GVC  Tumeur  Sclérose En Plaque  Classification Non Supervisée  Segmentation Floue  Distance Local et Non Local  Moyenne Non Local  Fetal MRI  Artifacts  Geodesic Active Contour  Tumor  Multiple Sclerosis  Classification Unsupervised  Fuzzy Segmentation  Local and Non-Local Distance  Non-Local Means  الصور بالرنين المغناطيسي للجنين  التقطيع  الاعطاب  الخطوط الجيوديسية النشطة  تحويل متجه التدرج )  (GVC ورم  التصلب المتعدد  تصنيف غير خاضع للرقابة  التجزئة غير واضحة  المسافة المحلية وغير المحلية  المتوسط غير محلي
Index. décimale :	621 Electronique
Résumé :	Fetal MRI is a complementary modality to ultrasound examination. The segmentation of fetal MRIs is a recent method, quickly becoming an essential step in many clinical applications for antenatal monitoring of maturation or brain malformation. However, the artifacts inherent in this type of image and the low resolution of these images are at the origin of the difficulties encountered in the segmentation of these images. To overcome these, we propose in this memory, two methods of segmentation: (i) the first one is based on the geodesic active contours applied to adult MRIs for the automatic detection of the brain lesions, (ii) the second one is based on the modification of the fuzzy segmentation to achieve the classification of fetal brain MRIs. The first method is a combination of the geodesic active contours function and the Gradient Vector Convolution (GVC) in order to improve the detection of the boundaries of the objects to be segmented. The model has been tested on adult MRIs that contain brain tumors or multiple sclerosis lesions. This model has been satisfactory in adults but not in the fetal case. This led us to use an unsupervised classification especially with fuzzy segmentation models. We have therefore, integrated the local and non-local distance in the term of attachment to the data of the RFCM (Robust Fuzzy C-Means) energy function, and integrate non-local means in the regularization term. An algorithm based on layer-bylayer segmentation of fetal brain regions, has been developed. Quantitative and qualitative results on real cerebral fetal images showed the efficacy and robustness of the proposed method compared to the methods described in the literature.
Diplôme :	Doctorat
En ligne :	../theses/electronique/ABD7321.pdf
Format de la ressource électronique :	pdf
Permalink :	index.php?lvl=notice_display&id=10971

Segmentation d’images de résonance magnétique de diffusion pour l’aide au diagnostic des pathologies cérébrales fœtales. [texte imprimé] / Mohamed Zaki Abderrezak, Auteur ; Karim Mansour, Directeur de thèse . - جامعة الإخوة منتوري قسنطينة, 2018 . -  ; 30 cm.
2 copies imprimées disponibles

Langues : Français (fre)

Catégories :	Français - Anglais Electronique
Tags :	IRM fœtale  Segmentation  Artefacts  Contour Active Géodésique  Gradient GVC  Tumeur  Sclérose En Plaque  Classification Non Supervisée  Segmentation Floue  Distance Local et Non Local  Moyenne Non Local  Fetal MRI  Artifacts  Geodesic Active Contour  Tumor  Multiple Sclerosis  Classification Unsupervised  Fuzzy Segmentation  Local and Non-Local Distance  Non-Local Means  الصور بالرنين المغناطيسي للجنين  التقطيع  الاعطاب  الخطوط الجيوديسية النشطة  تحويل متجه التدرج )  (GVC ورم  التصلب المتعدد  تصنيف غير خاضع للرقابة  التجزئة غير واضحة  المسافة المحلية وغير المحلية  المتوسط غير محلي
Index. décimale :	621 Electronique
Résumé :	Fetal MRI is a complementary modality to ultrasound examination. The segmentation of fetal MRIs is a recent method, quickly becoming an essential step in many clinical applications for antenatal monitoring of maturation or brain malformation. However, the artifacts inherent in this type of image and the low resolution of these images are at the origin of the difficulties encountered in the segmentation of these images. To overcome these, we propose in this memory, two methods of segmentation: (i) the first one is based on the geodesic active contours applied to adult MRIs for the automatic detection of the brain lesions, (ii) the second one is based on the modification of the fuzzy segmentation to achieve the classification of fetal brain MRIs. The first method is a combination of the geodesic active contours function and the Gradient Vector Convolution (GVC) in order to improve the detection of the boundaries of the objects to be segmented. The model has been tested on adult MRIs that contain brain tumors or multiple sclerosis lesions. This model has been satisfactory in adults but not in the fetal case. This led us to use an unsupervised classification especially with fuzzy segmentation models. We have therefore, integrated the local and non-local distance in the term of attachment to the data of the RFCM (Robust Fuzzy C-Means) energy function, and integrate non-local means in the regularization term. An algorithm based on layer-bylayer segmentation of fetal brain regions, has been developed. Quantitative and qualitative results on real cerebral fetal images showed the efficacy and robustness of the proposed method compared to the methods described in the literature.
Diplôme :	Doctorat
En ligne :	../theses/electronique/ABD7321.pdf
Format de la ressource électronique :	pdf
Permalink :	index.php?lvl=notice_display&id=10971