Flux maximizing geometric flows

Vasilevskiy, Alexander; Siddiqi, Kaleem

doi:10.1109/iccv.2001.937511

Cited by 137 publications

(237 citation statements)

References 37 publications

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“…This results in a leak when segmented with the type of flow (2). More sophisticated algorithms can be devised based on image statistics or prior knowledge such as multiscale filter responses tuned to detect vessels [13,6,14], but these algorithms will be very specific to the type of data and image acquisition. This leakage problem can be addressed in a more general way by adding a soft shape constraint to the flow so that the algorithm penalizes obvious leaks.…”

Section: Region Based Flowmentioning

confidence: 99%

Vessel Segmentation Using a Shape Driven Flow

Nain

Yezzi

Turk

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

116

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Abstract. We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branches. Using image intensity alone to deform a model for the task of segmentation often results in leakages at areas where the image information is ambiguous. To address this problem, we combine image statistics and shape information to derive a region-based active contour that segments tubular structures and penalizes leakages. We present results on synthetic and real 2D and 3D datasets.

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Section: Region Based Flowmentioning

confidence: 99%

Vessel Segmentation Using a Shape Driven Flow

Nain

Yezzi

Turk

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

116

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“…Although approaches in [5] and [6] are robust to intensity variation of objects and background, they are confused by the fluctuating gradient of object boundaries in such case. The locally defined flux cannot recover the weak edges that are longer than the radius of the target object.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with this problem, Vasilevskiy et al proposed the use of flux maximizing geometric flows for image segmentation [5]. Different from the above methods, object boundaries are detected by incorporating image gradient direction and magnitude.…”

Section: Introductionmentioning

confidence: 99%

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Minimal Weighted Local Variance as Edge Detector for Active Contour Models

Law

Chung

2006

Computer Vision – ACCV 2006

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Abstract. Performing segmentation of narrow, elongated structures with low contrast boundaries is a challenging problem. Boundaries of these structures are difficult to be located when noise exists or intensity of objects and background is varying. Using the active contour methods, this paper proposes a new vector field for detection of such structures. In this paper, unlike other work, object boundaries are not defined by intensity gradient but statistics obtained from a set of filters applied on an image. The direction and magnitude of edges are estimated such that the minimal weighted local variance condition is satisfied. This can effectively prevent contour leakage and discontinuity by linking disconnected boundaries with coherent orientation. It is experimentally shown that our method is robust to intensity variation in the image, and very suitable to deal with images with narrow structures and blurry edges, such as blood vessels.

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“…CURVES makes use of the minimal curvature to aid the detection of thin vessels. Vasilevskiy and Siddiqi [2] have introduced the image gradient-flux to deform surfaces for the segmentation of vascular structures. The image gradient-flux encapsulates both the image gradient magnitude and direction.…”

Section: Introductionmentioning

confidence: 99%

A Deformable Surface Model for Vascular Segmentation

Law

Chung

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Inspired by the motion of a solid surface under liquid pressure, this paper proposes a novel deformable surface model to segment blood vessels in medical images. In the proposed model, the segmented region and the background region are respectively considered as liquid and an elastic solid. The surface of the elastic solid experiences various forces derived from the second order intensity statistics and the surface geometry. These forces cause the solid surface to deform in order to segment vascular structures in an image. The proposed model has been studied in the experiments on synthetic data and clinical data acquired by different imaging modalities. It is experimentally shown that the new model is robust to intensity contrast changes inside blood vessels and thus very suitable to perform vascular segmentation.

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Flux maximizing geometric flows

Cited by 137 publications

References 37 publications

Vessel Segmentation Using a Shape Driven Flow

Vessel Segmentation Using a Shape Driven Flow

Minimal Weighted Local Variance as Edge Detector for Active Contour Models

A Deformable Surface Model for Vascular Segmentation

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