Level Set Method Thesis

Changes in retinal structure are related to different eye diseases.Various retinal imaging techniques, such as fundus imaging and optical coherence tomography (OCT) imaging modalities, have been developed for non-intrusive ophthalmology diagnoses according to the vasculature changes.The performance of our method is demonstrated on a variety of synthetic and real textured frames.

Thus, the overall glioma segmentation turns into an efficient, nearly real time process with intuitive and usefully restricted user interaction.

The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model.

The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE.

The aim of this Ph D thesis is to develop segmentation methods to extract clinically useful information from these retinal images, which are acquired from different imaging modalities.

In other words, we built the segmentation methods to extract important structures from both 2D fundus images and 3D OCT images.

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