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.
