000203512 001__ 203512
000203512 005__ 20190317000044.0
000203512 0247_ $$2doi$$a10.1109/Tpami.2015.2462363
000203512 022__ $$a0162-8828
000203512 02470 $$2ISI$$a000377897100005
000203512 037__ $$aARTICLE
000203512 245__ $$aMultiscale Centerline Detection
000203512 269__ $$a2016
000203512 260__ $$bInstitute of Electrical and Electronics Engineers$$c2016$$aLos Alamitos
000203512 300__ $$a15
000203512 336__ $$aJournal Articles
000203512 520__ $$aFinding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi, and dendritic arbors in 3D biomedical image stacks. Existing techniques rely either on filters designed to respond to ideal cylindrical structures or on classification techniques. The former tend to become unreliable when the linear structures are very irregular while the latter often has difficulties distinguishing centerline locations from neighboring ones, thus losing accuracy. We solve this problem by reformulating centerline detection in terms of a \emph{regression} problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets. Moreover, our approach is very generic and also performs well on contour detection. We show an improvement above recent contour detection algorithms on the BSDS500 dataset.
000203512 6531_ $$acenterline detection
000203512 6531_ $$alinear structures
000203512 6531_ $$amultiscale detection
000203512 6531_ $$aradial estimation
000203512 6531_ $$aregression
000203512 6531_ $$aneuron tracing
000203512 6531_ $$aroad tracing
000203512 6531_ $$aautomated reconstruction
000203512 6531_ $$aboundary detection
000203512 700__ $$0246639$$g221807$$aSironi, Amos
000203512 700__ $$0242718$$g183992$$aTüretken, Engin
000203512 700__ $$g149007$$aLepetit, Vincent$$0240235
000203512 700__ $$aFua, Pascal$$g112366$$0240252
000203512 773__ $$j38$$tIEEE Transactions on Pattern Analysis and Machine Intelligence$$k7$$q1327--1341
000203512 8564_ $$uhttps://infoscience.epfl.ch/record/203512/files/preprint_1.pdf$$zPreprint$$s14903721$$yPreprint
000203512 909C0 $$xU10659$$0252087$$pCVLAB
000203512 909CO $$qGLOBAL_SET$$pIC$$ooai:infoscience.tind.io:203512$$particle
000203512 917Z8 $$x221807
000203512 917Z8 $$x112366
000203512 917Z8 $$x112366
000203512 917Z8 $$x112366
000203512 937__ $$aEPFL-ARTICLE-203512
000203512 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000203512 980__ $$aARTICLE