RORPO : A morphological framework for curvilinear structure analysis; Application to the filtering and segmentation of blood vessels

Abstract : The analysis of curvilinear structures in 3D images is a complex and challenging task. Curvilinear structures are thin, easily corrupted by noise and present a complex geometry. Despite the numerous applications in material sciences, remote sensing and medical imaging and the large number of dedicated methods developed the last few years, the detection of such structures remains a difficult problem.In this thesis, we work on the characterization of curvilinear structures. We first propose a new framework called RORPO, to characterize such structures through two features: an intensity feature which preserves the intensity of curvilinear structures while decreasing the intensity of other structures, and a directional feature providing at each point, the direction of the curvilinear structure.RORPO, unlike classic other state of the art methods, is non-local and non-linear, which are desirable properties adapted to the intrinsic anisotropy of curvilinear structures. RORPO is based on recent advances in mathematical morphology: the path operators.We provide a full description of the structural and algorithmic details of RORPO, and we also conduct a quantitative comparative study of our features with three popular curvilinear structure analysis filters: the Frangi Vesselness, the Optimally Oriented Flux, and the Hybrid Diffusion with Continuous Switch.Besides the straightforward filtering application, both RORPO features are designed to be used as prior information to characterize curvilinear structures. We propose a regularization term for variational segmentation which embed these features. Classic regularization terms are not adapted to curvilinear structures and usually lead to the loss of most of the low-contrasted ones. We propose to only regularize curvilinear structures along their main axis thanks to both RORPO features. This directional regularization better preserves curvilinear structures but also reconnect parts of these structures which may have been disconnected by noise.We present results of the segmentation of retinal images with the Chan et al. model either with the classic total variation or our directional regularization term. This confirm that our regularization term is better suited for images with curvilinear structures
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Odyssée Merveille. RORPO : A morphological framework for curvilinear structure analysis; Application to the filtering and segmentation of blood vessels. Computation and Language [cs.CL]. Université Paris-Est, 2016. English. ⟨NNT : 2016PESC1056⟩. ⟨tel-01531160⟩



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