Medical Image Segmentation Using Magnetostatic Active Contours (MAC) with Tensor Diffusion

Huaizhong Zhang, Xianghua Xie

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

Abstract

In medical imagery, traditional deformable models often face substantial challenges due to fine structures and image complexity. Recently, based on magnetostatic theory, a new deformable model, namely MAC, is proposed for improving the ability of the active contour in dealing with complex geometries and segmentation difficulties. A Laplacian diffusion scheme is proposed in the MAC model to tackle excessive image noise which can interrupt image gradient vectors and in turn affect the external force field. In this paper, a derived vector potential field (VPF) is employed to obtain magnetic force and thus a diffusion tensor can be applied to diffuse VPF in terms of both magnitude and directional information, instead of directly diffusing the magnetic field as in the MAC model. Our diffusion is carried out both in spatial and temporal aspects of VPF so that the performance of the deformable model is significantly improved while images are with low signal-noise ratio (SNR) and poor contrast. In addition, the proposed diffusion enhancement can lead to evolving the curve smoothly and thus level set evolution is adapted to approach genuine object of interest. By applying in several medical image modalities, the results demonstrate the effectiveness of the proposed method.
Original languageEnglish
Title of host publicationNot Known
Publication statusPublished - 2012
Event16th Medical Image Understanding and Analysis - Swansea, United Kingdom
Duration: 9 Jul 201211 Jul 2012

Conference

Conference16th Medical Image Understanding and Analysis
CountryUnited Kingdom
CitySwansea
Period9/07/1211/07/12

Fingerprint

Image segmentation
Tensors
Magnetic fields
Geometry

Cite this

@inproceedings{8d5c76c8824442e8a80eec9605598f0f,
title = "Medical Image Segmentation Using Magnetostatic Active Contours (MAC) with Tensor Diffusion",
abstract = "In medical imagery, traditional deformable models often face substantial challenges due to fine structures and image complexity. Recently, based on magnetostatic theory, a new deformable model, namely MAC, is proposed for improving the ability of the active contour in dealing with complex geometries and segmentation difficulties. A Laplacian diffusion scheme is proposed in the MAC model to tackle excessive image noise which can interrupt image gradient vectors and in turn affect the external force field. In this paper, a derived vector potential field (VPF) is employed to obtain magnetic force and thus a diffusion tensor can be applied to diffuse VPF in terms of both magnitude and directional information, instead of directly diffusing the magnetic field as in the MAC model. Our diffusion is carried out both in spatial and temporal aspects of VPF so that the performance of the deformable model is significantly improved while images are with low signal-noise ratio (SNR) and poor contrast. In addition, the proposed diffusion enhancement can lead to evolving the curve smoothly and thus level set evolution is adapted to approach genuine object of interest. By applying in several medical image modalities, the results demonstrate the effectiveness of the proposed method.",
author = "Huaizhong Zhang and Xianghua Xie",
year = "2012",
language = "English",
booktitle = "Not Known",

}

Zhang, H & Xie, X 2012, Medical Image Segmentation Using Magnetostatic Active Contours (MAC) with Tensor Diffusion. in Not Known. 16th Medical Image Understanding and Analysis, Swansea, United Kingdom, 9/07/12.

Medical Image Segmentation Using Magnetostatic Active Contours (MAC) with Tensor Diffusion. / Zhang, Huaizhong; Xie, Xianghua.

Not Known. 2012.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

TY - GEN

T1 - Medical Image Segmentation Using Magnetostatic Active Contours (MAC) with Tensor Diffusion

AU - Zhang, Huaizhong

AU - Xie, Xianghua

PY - 2012

Y1 - 2012

N2 - In medical imagery, traditional deformable models often face substantial challenges due to fine structures and image complexity. Recently, based on magnetostatic theory, a new deformable model, namely MAC, is proposed for improving the ability of the active contour in dealing with complex geometries and segmentation difficulties. A Laplacian diffusion scheme is proposed in the MAC model to tackle excessive image noise which can interrupt image gradient vectors and in turn affect the external force field. In this paper, a derived vector potential field (VPF) is employed to obtain magnetic force and thus a diffusion tensor can be applied to diffuse VPF in terms of both magnitude and directional information, instead of directly diffusing the magnetic field as in the MAC model. Our diffusion is carried out both in spatial and temporal aspects of VPF so that the performance of the deformable model is significantly improved while images are with low signal-noise ratio (SNR) and poor contrast. In addition, the proposed diffusion enhancement can lead to evolving the curve smoothly and thus level set evolution is adapted to approach genuine object of interest. By applying in several medical image modalities, the results demonstrate the effectiveness of the proposed method.

AB - In medical imagery, traditional deformable models often face substantial challenges due to fine structures and image complexity. Recently, based on magnetostatic theory, a new deformable model, namely MAC, is proposed for improving the ability of the active contour in dealing with complex geometries and segmentation difficulties. A Laplacian diffusion scheme is proposed in the MAC model to tackle excessive image noise which can interrupt image gradient vectors and in turn affect the external force field. In this paper, a derived vector potential field (VPF) is employed to obtain magnetic force and thus a diffusion tensor can be applied to diffuse VPF in terms of both magnitude and directional information, instead of directly diffusing the magnetic field as in the MAC model. Our diffusion is carried out both in spatial and temporal aspects of VPF so that the performance of the deformable model is significantly improved while images are with low signal-noise ratio (SNR) and poor contrast. In addition, the proposed diffusion enhancement can lead to evolving the curve smoothly and thus level set evolution is adapted to approach genuine object of interest. By applying in several medical image modalities, the results demonstrate the effectiveness of the proposed method.

M3 - Conference proceeding (ISBN)

BT - Not Known

ER -