&lt;title&gt;Interactive outlining: an improved approach using active contours&lt;/title&gt;

Daneels, Dirk; Campenhout, David van; Niblack, Carlton Wayne; Equitz, W.; Barber, Ronald; Fierens, Freddy

doi:10.1117/12.143652

Cited by 47 publications

(22 citation statements)

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“…In [41], Daneels et al developed an improved method of active contours. Based on the user's input, the algorithm first used a greedy procedure to provide fast initial convergence.…”

Section: Segmentationmentioning

confidence: 99%

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Rui¹,

Huang²,

Chang³

1999

Journal of Visual Communication and Image Representation

1,580

776

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This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C

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“…In [41], Daneels et al developed an improved method of active contours. Based on the user's input, the algorithm first used a greedy procedure to provide fast initial convergence.…”

Section: Segmentationmentioning

confidence: 99%

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Rui¹,

Huang²,

Chang³

1999

Journal of Visual Communication and Image Representation

1,580

776

View full text Add to dashboard Cite

show abstract

“…Letting D(p) be the unit vector perpendicular (rotated 90 degrees clockwise) to the gradient direction at point p (i.e., for D(p) = (I y (p), -I x (p))), the formulation of the gradient direction feature cost is (4) where are vector dot products and (5) is the bidirectional link or edge vector between pixels p and q. Links are either horizontal, vertical, or diagonal (relative to the position of q in p's neighborhood) and point such that the dot product of D(p) and L(p, q) is positive, as noted in (5). The neighborhood link direction associates a high cost to an edge or link between two pixels that have similar gradient directions but are perpendicular, or near perpendicular, to the link between them.…”

Section: Image Feature Formulationmentioning

confidence: 99%

“…Other popular boundary definition methods use active contours or snakes [1,5,8,15] to improve a manually entered rough approximation. After being initialized with a rough boundary approximation, snakes iteratively adjust the boundary points in parallel in an attempt to minimize an energy functional and achieve an optimal boundary.…”

Section: Introductionmentioning

confidence: 99%

Intelligent scissors for image composition

Mortensen

Barrett

1995

Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH '95

650

382

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We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary "snaps" to, and wraps around the object of interest.Live-wire boundary detection formulates discrete dynamic programming (DP) as a two-dimensional graph searching problem. DP provides mathematically optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted.Extracted objects can be scaled, rotated, and composited using live-wire masks and spatial frequency equivalencing. Frequency equivalencing is performed by applying a Butterworth filter which matches the lowest frequency spectra to all other image components. Intelligent Scissors allow creation of convincing compositions from existing images while dramatically increasing the speed and precision with which objects can be extracted.

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“…The performance not only relies on the design of the iterative optimization algorithm, but also depends on the initial boundary data, which is usually defined by a user or from a priori knowledge. 3,4,8,9 Since the user interaction is not allowed once the iterative process starts, the outcome might not be predictable. And the whole process will be repeated from the beginning when inadequate segmentation is obtained.…”

mentioning

confidence: 99%

Live-wire-based segmentation of (3D) anatomical structures for image-guided lung interventions

Lü

Zhang

et al. 2012

SPIE Proceedings

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Computed Tomography (CT) has been widely used for assisting in lung cancer detection/diagnosis and treatment. In lung cancer diagnosis, suspect lesions or regions of interest (ROIs) are usually analyzed in screening CT scans. Then, CT-based image-guided minimally invasive procedures are performed for further diagnosis through bronchoscopic or percutaneous approaches. Thus, ROI segmentation is a preliminary but vital step for abnormality detection, procedural planning, and intra-procedural guidance. In lung cancer diagnosis, such ROIs can be tumors, lymph nodes, nodules, etc., which may vary in size, shape, and other complication phenomena. Manual segmentation approaches are time consuming, user-biased, and cannot guarantee reproducible results. Automatic methods do not require user input, but they are usually highly application-dependent. To counterbalance among efficiency, accuracy, and robustness, considerable efforts have been contributed to semiautomatic strategies, which enable full user control, while minimizing human interactions. Among available semi-automatic approaches, the live-wire algorithm has been recognized as a valuable tool for segmentation of a wide range of ROIs from chest CT images. In this paper, a new 3D extension of the traditional 2D live-wire method is proposed for 3D ROI segmentation. In the experiments, the proposed approach is applied to a set of anatomical ROIs from 3D chest CT images, and the results are compared with the segmentation derived from a previous evaluated live-wire-based approach.

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<title>Interactive outlining: an improved approach using active contours</title>

Cited by 47 publications

References 0 publications

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Intelligent scissors for image composition

Live-wire-based segmentation of (3D) anatomical structures for image-guided lung interventions

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