User-steered image segmentation paradigms: Live wire and live lane

dc.creatorFalcao, AX
dc.creatorUdupa, JK
dc.creatorSamarasekera, S
dc.creatorSharma, S
dc.creatorHirsch, BE
dc.creatorLotufo, RDA
dc.date1998
dc.dateJUL
dc.date2014-12-02T16:25:41Z
dc.date2015-11-26T16:19:17Z
dc.date2014-12-02T16:25:41Z
dc.date2015-11-26T16:19:17Z
dc.date.accessioned2018-03-28T23:02:19Z
dc.date.available2018-03-28T23:02:19Z
dc.identifierGraphical Models And Image Processing. Academic Press Inc, v. 60, n. 4, n. 233, n. 260, 1998.
dc.identifier1077-3169
dc.identifierWOS:000074580800001
dc.identifier10.1006/gmip.1998.0475
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/73049
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/73049
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/73049
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1267671
dc.descriptionIn multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide effective control to the user on the segmentation process while it is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to as live wire and live lane, for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its "boundariness," and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor, If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. in live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary. We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5-2.5 times faster than manual tracing. (C) 1998 Academic Press.
dc.description60
dc.description4
dc.description233
dc.description260
dc.languageen
dc.publisherAcademic Press Inc
dc.publisherSan Diego
dc.publisherEUA
dc.relationGraphical Models And Image Processing
dc.relationGraph. Models Image Process.
dc.rightsfechado
dc.sourceWeb of Science
dc.subjectimage segmentation
dc.subjectinteractive segmentation
dc.subjectboundary detection
dc.subjectmedical imaging
dc.subjectvisualization
dc.subjectevaluation of segmentation
dc.subjectMr Images
dc.subjectSystem
dc.subjectHead
dc.titleUser-steered image segmentation paradigms: Live wire and live lane
dc.typeArtículos de revistas


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