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Auteurs: CHIKHAOUI Ahmed et Abdelkader Djillali

Ecole/Université: Université Abou Bakr Belkaïd de Tlemcen

Résumé: Dans le domaine du diagnostic médical, les intéressés ont recours de plus en plus à l’imagerie médicale ; il est bien établi que la justesse et la précision du diagnostic sont initialement liées à la qualité de l’image ; Mais la qualité de l’image est elle-même conditionnée par un certain nombre de facteurs dont principalement les traitements que doit subir une image pour rehausser sa qualité. Nous nous sommes intéressés aux ondelettes et paquet d’ondelette, afin de compresser des images médicales (IRM). Les résultats obtenus sont très satisfaisant du point de vue taux de compression et qualité d’image comprimée par rapport aux méthodes classique ; et encourageants comparativement aux résultats publiés.

Extrait du sommaire:
Introduction générale
Chapitre І : Etat de l’art sur la compression d’images médicales
I. Introduction
II. But de la compression d’image
III. Compression sans perte d’informations
1. Codage entropique
i. Codage de Huffman
ii. Codage Arithmétique
2. Codage par Plages De Zéros (RLE)
3. Codage Lempel-Zif-Welsh (LZW)
IV. Compression avec perte d’informations
1. Quantification
i. Quantification scalaire
ii. Quantification vectorielle
2. Compression par transformation
3. Codage par prédiction
4. Codage des sous-bandes
i. Algorithme EZW
ii. Algorithme SPIHT
iii. Algorithmes EQ
V. Evaluation de la qualité de compression
i. Taux de compression
ii. Taux d’information
iii. Mesures de distorsion
VI. Etat de l’art sur la compression d’images médicales
VII. Conclusion
Chapitre II : Transformée en Ondelettes
I. Introduction
II. Principe des ondelettes
III. Transformée en ondelettes
1. Transformée en ondelettes continue CWT
2. Transformée en ondelettes discrète DWT
IV. Analyse Multi Résolution
1. Définition
2. Construction d’Ondelettes à partir de l’analyse multi résolution
a. Fonction d’échelle
b. Ondelette
c. Les filtre H et G
3. Propriétés Fondamentales d’une Ondelette
V. Extension de la transformée en ondelettes aux signaux bidimensionnels
VI. Analyse
1. Décomposition
2. Reconstruction des lignes
3. Quelques exemples d’ondelettes
i. Ondelette de Haar
ii. Ondelette de Morlet
iii. Ondelette de Mexican Hat
iv. Ondelette de Shannon
VII. Ondelettes Bi orthogonales
1. Intérêt
2. Inconvénient
VIII. Conclusion
Chapitre III : Transformée en Paquets d’ondelettes
I. Introduction
II. Analyse Multi Résolution
III. Théorie de l’AMR
a) Définition
b) Interprétations
IV. Ondelettes et fonctionsd’échelle
a) Espaces de détails
b) Bancs de filtres à reconstruction parfaite et algorithme à trous
c) De l’algorithme à trous aux fonctions d’échelle
V. Algorithme
VI. Avantages et applications
VII. Application aux images
a) Algorithme pyramidal
b) Filtrage par bande
VIII. Conclusion
Chapitre VI : Résultats des expériences
I. Introduction
II. Résultats de simulations
1. Performances du SPIHT et EZW
2. Choix de la meilleure ondelette
a) Application 1 : Ondelettes Debauchies
b) Application 2 : Ondelette Coiflet et Symlet
c) Application 3: Ondelette Biorthogonale
Comparaison des résultats
3. Influence du niveau de compression d’ondelette couplé avec le SPIHT
4. Compression d’images médicales par PWT couplée avec SPIHT
III. Conclusion
Conclusion Générale

Ondelettes et traitement du signal et d’image 20


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