University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Titre : Contribution à la segmentation d’images: application en imagerie médicale Type de document : document électronique Auteurs : Imane Mehidi, Auteur ; Belkhiat, Djamel Eddine Chouaib, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (112 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Physique Mots-clés : image processing segmentation classification retinal blood vessels Index. décimale : 530 Physique Résumé : Retinal imaging is a powerful tool for detecting and diagnosing various health conditions.
Locating retinal vessels is important because it allows for the specification of
different tissues of the vascular structure. Ophthalmologists use images with binary
segmentation of retinal fundus to analyze and predict diseases such as hypertension
and diabetes. However, blood vessel segmentation in retinal images can be challenging
due to several factors such as low contrast, background illumination inhomogeneity,
and noise.
The main objective of this dissertation is to study and propose effective methods
for automatic retinal vasculature segmentation. Our first contribution involved studying
the performance of improved FCM algorithms, including FCM, EnFCM, SFCM,
FGFCM, FRFCM, DSFCM_N, FCM_SICM, and SSFCA, to recommend the best ones
for the segmentation of retinal blood vessels. We evaluated their performance based on
three criteria: noise robustness, blood vessel segmentation performance, and execution
time.
In our second contribution, we proposed a new unsupervised method that ensures
high-accuracy detection compared to previous studies. It depends on hybrid filtering
and adaptive thresholding. We validated our proposed studies using two benchmark
databases: STARE and DRIVE.
This dissertation also includes contributions related to the segmentation of MR
brain images to identify tumors and different tissues. These contributions involve the
development of new methods that have been evaluated using various databases and
have shown promising results. These contributions are included in the appendices of
the dissertation.
Overall, this dissertation aims to contribute to the field of medical image segmentation
by proposing effective new methods, which can help in disease detection and
monitoring.Côte titre : DPH/0297 En ligne : http://dspace.univ-setif.dz:8888/jspui/bitstream/123456789/4360/1/2302.pdf Contribution à la segmentation d’images: application en imagerie médicale [document électronique] / Imane Mehidi, Auteur ; Belkhiat, Djamel Eddine Chouaib, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (112 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Physique Mots-clés : image processing segmentation classification retinal blood vessels Index. décimale : 530 Physique Résumé : Retinal imaging is a powerful tool for detecting and diagnosing various health conditions.
Locating retinal vessels is important because it allows for the specification of
different tissues of the vascular structure. Ophthalmologists use images with binary
segmentation of retinal fundus to analyze and predict diseases such as hypertension
and diabetes. However, blood vessel segmentation in retinal images can be challenging
due to several factors such as low contrast, background illumination inhomogeneity,
and noise.
The main objective of this dissertation is to study and propose effective methods
for automatic retinal vasculature segmentation. Our first contribution involved studying
the performance of improved FCM algorithms, including FCM, EnFCM, SFCM,
FGFCM, FRFCM, DSFCM_N, FCM_SICM, and SSFCA, to recommend the best ones
for the segmentation of retinal blood vessels. We evaluated their performance based on
three criteria: noise robustness, blood vessel segmentation performance, and execution
time.
In our second contribution, we proposed a new unsupervised method that ensures
high-accuracy detection compared to previous studies. It depends on hybrid filtering
and adaptive thresholding. We validated our proposed studies using two benchmark
databases: STARE and DRIVE.
This dissertation also includes contributions related to the segmentation of MR
brain images to identify tumors and different tissues. These contributions involve the
development of new methods that have been evaluated using various databases and
have shown promising results. These contributions are included in the appendices of
the dissertation.
Overall, this dissertation aims to contribute to the field of medical image segmentation
by proposing effective new methods, which can help in disease detection and
monitoring.Côte titre : DPH/0297 En ligne : http://dspace.univ-setif.dz:8888/jspui/bitstream/123456789/4360/1/2302.pdf Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité DPH/0297 DPH/0297 Thèse Bibliothéque des sciences Français Disponible
Disponible