000232493 001__ 232493
000232493 005__ 20190509132620.0
000232493 0247_ $$2doi$$a10.5075/epfl-thesis-7951
000232493 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7951-5
000232493 02471 $$2nebis$$a11075646
000232493 037__ $$aTHESIS
000232493 041__ $$aeng
000232493 088__ $$a7951
000232493 245__ $$bA Tale of Points and Curves$$aLandmark Active Contours for Bioimage Analysis
000232493 260__ $$bEPFL$$c2017$$aLausanne
000232493 269__ $$a2017
000232493 300__ $$a224
000232493 336__ $$aTheses
000232493 502__ $$aProf. Pierre Vandergheynst (président) ; Prof. Michaël Unser (directeur de thèse) ; Prof. Dimitri Van De Ville, Prof. Carolina Wählby, Prof. Fred Hamprecht (rapporteurs)
000232493 520__ $$aThe problem of identifying the outline of objects in images can be approached from two starting points, either by considering localized features (landmarks, keypoints or regions), or by searching for global contours. 
 Features are regions or points of interest and usually include a description of the local properties of the image around them. The definition of a feature is flexible. Most often, it consists of a list of desirable properties inspired by the application at hand. Algorithms are then designed to robustly detect occurrences of the feature in the image under the effect of various geometrical transformations.
 Contours, on the other hand, are (portions of) curves that can be delineated using deformable models, for instance relying on spline curves. Splines are in particular at the core of a large family of such models called spline-based active contours, or designer snakes. These methods can be customized and adapted to outline a large variety of objects in many types of images. 
 
 In this thesis, we aim at unifying these two strategies by bridging automated feature detection and spline-based active contour segmentation for bioimage analysis. Our work proceeds in three steps. First, we introduce and characterize the Hermite spline interpolation framework, a model that allows incorporating local information at each node in the spline curve. Then, we study the design of custom feature detectors based on the steerable filters formalism. With these two ingredients, we propose a semiautomated segmentation algorithm called the landmark snake, which is defined relying on Hermite interpolation and evolves a curve in the image to outline objects of interest using information provided by steerable features detectors. The Hermite spline formalism allows for a direct correspondence between control points on the spline curve and landmarks, simplifying the algorithm design and allowing for user-friendly interactions. The approach is generic enough to be used in a wide variety of data, as will be illustrated through real bioimage analysis problems in the context of collaborative work with external laboratories.
000232493 6531_ $$aBioimage analysis
000232493 6531_ $$asegmentation
000232493 6531_ $$acontour detection
000232493 6531_ $$aoutlining
000232493 6531_ $$afeature detectors
000232493 6531_ $$akeypoint detectors
000232493 6531_ $$aactive contours
000232493 6531_ $$aparametric models
000232493 6531_ $$aHermite interpolation
000232493 6531_ $$aspline-based methods
000232493 700__ $$0246744$$g181475$$aUhlmann, Virginie Sophie
000232493 720_2 $$aUnser, Michaël$$edir.$$g115227$$0240182
000232493 8564_ $$uhttps://infoscience.epfl.ch/record/232493/files/EPFL_TH7951.pdf$$s25176132
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000232493 909CO $$pthesis$$pthesis-bn2018$$pDOI$$ooai:infoscience.tind.io:232493$$qDOI2$$qGLOBAL_SET$$pSTI
000232493 917Z8 $$x108898
000232493 918__ $$dEDEE$$cIMT$$aSTI
000232493 919__ $$aLIB
000232493 920__ $$b2017$$a2017-12-1
000232493 970__ $$a7951/THESES
000232493 973__ $$sPUBLISHED$$aEPFL
000232493 980__ $$aTHESIS