Vesselness enhancement diffusion

C. Canero; P. Radeva

doi:10.1016/j.patrec.2003.08.001

Vesselness enhancement diffusion

C. Canero, P. Radeva

Research output: Contribution to journal › Article › Research › peer-review

44 Citations (Scopus)

Abstract

We propose a new anisotropic diffusion filter to enhance the local coherence of multiscale tubular structures on 2D images. The proposed filter uses a diffusion tensor with diffusion direction and strength determined by the local structure, and chooses automatically for each pixel the diffusion tensor scale. In this paper, we show how this filter enhances X-ray coronary angiographic images to facilitate vessel segmentation. To this aim, we present experimental results of the performance of the filter on synthetic and real images. © 2003 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	3141-3151
Journal	Pattern Recognition Letters
Volume	24
Issue number	16
DOIs	https://doi.org/10.1016/j.patrec.2003.08.001
Publication status	Published - 1 Jan 2003

Keywords

Image enhancement
Non-linear filtering
Tubular structures enhancement
Vessel segmentation
X-ray angiography

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title = "Vesselness enhancement diffusion",

abstract = "We propose a new anisotropic diffusion filter to enhance the local coherence of multiscale tubular structures on 2D images. The proposed filter uses a diffusion tensor with diffusion direction and strength determined by the local structure, and chooses automatically for each pixel the diffusion tensor scale. In this paper, we show how this filter enhances X-ray coronary angiographic images to facilitate vessel segmentation. To this aim, we present experimental results of the performance of the filter on synthetic and real images. {\textcopyright} 2003 Elsevier B.V. All rights reserved.",

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AU - Radeva, P.

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AB - We propose a new anisotropic diffusion filter to enhance the local coherence of multiscale tubular structures on 2D images. The proposed filter uses a diffusion tensor with diffusion direction and strength determined by the local structure, and chooses automatically for each pixel the diffusion tensor scale. In this paper, we show how this filter enhances X-ray coronary angiographic images to facilitate vessel segmentation. To this aim, we present experimental results of the performance of the filter on synthetic and real images. © 2003 Elsevier B.V. All rights reserved.

KW - Image enhancement

KW - Non-linear filtering

KW - Tubular structures enhancement

KW - Vessel segmentation

KW - X-ray angiography

U2 - 10.1016/j.patrec.2003.08.001

DO - 10.1016/j.patrec.2003.08.001

M3 - Article

VL - 24

SP - 3141

EP - 3151

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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IS - 16

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