An ultra-fast user-steered image segmentation paradigm: live wire on the fly

By Alexandre Xavier Falcão, Jayaram Koteshwar Udupa, Flavio Keidi Miyazawa. IEEE Transactions on Medical Imaging, 2000

334 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cefc45b6b40455d12b56f66e11cfd8b31a2b7f5b

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In Computer Vision and Pattern Recognition

Citations in the corpus

Segmentation (2019, background): “K ], K |N|, are integers, the graph is sparse, and Q uses bucket sorting [8].”
The image foresting transform: theory, algorithms, and applications (2004, methodology): “The IFT approach covers both formulations and several interesting interactive extensions—such as live-wire [17], [35], [18], [19] and live-lane [17].”
A comparative study of deformable contour methods on medical image segmentation (2008, methodology): “We did not consider other methods incorporating with the a priori knowledge of object shape [15–17,21], random process [12–14], or human interaction [48,49] in the contour deformation processes because they are generally application specific and cannot be compared fairly.”
A survey of graph theoretical approaches to image segmentation (2013, background): “[92], who exploited some known properties of graphs to avoid the unnecessary shortest path computation and proposed a fast algorithm called live-wire-on-the-fly.”
Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images (2004, methodology): “Optimal paths based on this cost function are computed by a version of Dijkstra’s shortest-path algorithm (30) based on a discretization of the cost values and the use of a circular priority queue (19,20,22).”
User-Friendly Interactive Image Segmentation Through Unified Combinatorial User Inputs (2010, methodology): “Early interactive image segmentation algorithms utilize either regional properties such as Adobe’s magic wand or boundary properties such as active contour [5] and intelligent scissors [6], [7].”
Analysis of vasculature for liver surgical planning (2002, methodology): “1) Liver Segmentation:To extract the liver , we developed semiautomatic segmentation tools (based on live-wire techniques [10], [30], [49]).”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, neutral): “Live wire and intelligent scissors [13], [14], [229]–[232] use the minimal path approach to design an effective interactive tool with graphical interface for intelligently tracking an object boundary.”
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach (2006, background): “Two-dimensional boundary-based segmentation utilizing graph-searching principles [1], [2], [3], [4], [5], [6], [7] has become one of the best understood and frequently utilized medical image segmentation tools.”