3D vasculature segmentation using localized hybrid level-set method.

Qingqi Hong,Qingqiang Wu,Kunhong Liu,Beizhan Wang,Junfeng Yao,Yan Li,Qingde Li

doi:10.1186/1475-925x-13-169

Qingqi Hong, Qingqiang Wu + Show 5 more

Open Access

https://doi.org/10.1186/1475-925x-13-169

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Abstract

BackgroundIntensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.MethodsThis paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.ResultsExperiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.ConclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

Highlights

Intensity inhomogeneity occurs in many medical images, especially in vessel images
The original hybrid model utilized a preset global threshold indicating the lower bound of target object to specify the term concerning regional information, which is not quite appropriate, especially for medical images with intensity inhomogeneity
Our proposed localized hybrid level-set model utilizes the automatically calculated dynamic thresholds to indicate the lower bound of target object to specify the term concerning regional information instead of using a preset global threshold, which is crucial for the segmentation of vessel images [15]

Summary

Introduction

Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. The first task to vessel reconstruction is to identify the vessel region from initial vessel images This is usually achieved by using the technique of segmentation, which plays an important role in medical image processing. A multitude of powerful methods based on parametrical deformable model have been proposed for the segmentation of medical image [7]. Klein et al [8] presented a method for the extraction of vessel boundaries using deformable surface models represented by B-spline functions. Some of the well-known models are the geodesic active contour model [11] that utilizes image gradient to stop evolving contours on the object boundaries, and Chan-Vese model [12] that solves the leakage problem by using region information of the target boundary for segmentation. Some techniques combining edge and region information [4,15,16] have been developed and applied to segmentation of medical imaging

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Results

Conclusion