Extraction of vessel networks based on multiview projection and phase field model

Zhao, Shu; Zhou, Mingquan; Tian, Yun; Xu, Pengfei; Wu, Zhang; Deng, Qi

doi:10.1016/j.neucom.2015.03.048

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…So we first project the volume onto the 2D plane along the -axis. According to our previous work [19], the volume projected back from the the -axis direction is the largest. The segmentation on MIP images builds on our previous work [20].…”

Section: Background Subtraction and Vessel Reservationmentioning

confidence: 84%

Vascular Extraction Using MRA Statistics and Gradient Information

Zhao

Tian

Wang

et al. 2018

Mathematical Problems in Engineering

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Brain vessel segmentation is a fundamental component of cerebral disease screening systems. However, detecting vessels is still a challenging task owing to their complex appearance and thinning geometry as well as the contrast decrease from the root of the vessel to its thin branches. We present a method for segmentation of the vasculature in Magnetic Resonance Angiography (MRA) images. First, we apply volume projection, 2D segmentation, and back-projection procedures for first stage of background subtraction and vessel reservation. Those labeled as background or vessel voxels are excluded from consideration in later computation. Second, stochastic expectation maximization algorithm (SEM) is used to estimate the probability density function (PDF) of the remaining voxels, which are assumed to be mixture of one Rayleigh and two Gaussian distributions. These voxels can then be classified into background, middle region, or vascular structure. Third, we adapt the -means method which is based on the gradient of remaining voxels to effectively detect true positives around boundaries of vessels. Experimental results on clinical cerebral data demonstrate that using gradient information as a further step improves the mixture model based segmentation of cerebral vasculature, in particular segmentation of the low contrast vasculature.

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Section: Background Subtraction and Vessel Reservationmentioning

confidence: 84%

Vascular Extraction Using MRA Statistics and Gradient Information

Zhao

Tian

Wang

et al. 2018

Mathematical Problems in Engineering

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“…Extraction methods using the active contour model have been an active research area, and the research literature have exhibited a large variety of deformable model analysis [17], [18]. For example, Sun et al [19] proposed an active contour model using local morphology fitting for vessel segmentation on 2D angiogram images.…”

Section: Related Workmentioning

confidence: 99%

Intracranial Vascular Structure Extraction: A Machine Learning Approach

Zhao

Wang

Xie³

et al. 2019

IEEE Access

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Extraction of brain blood vessels is an important issue for clinical assessment of intracranial vascular diseases. In this paper, the vessel extraction problem is formulated to a connected region classification problem. In processing images, an improved multi-scale filtering method is performed to improve vessel connectivity, and a post-processing step is added to harvest salient vessel candidates (SVC). Then, SVC is decomposed into connected regions and features are calculated and fed into a neural network classifier for training. For extraction, each connected region is individually analyzed using the trained neural network by considering the values of neighboring voxels that belong to its connection. The extraction results demonstrate the validity of the proposed approach.

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“…3 gives the detecting results performed by multi-scale framework, where Fig. 3 (a) is an original liver vessel image obtained by previous works [22,23] [1,2], [1,4], and [1,7], respectively.…”

Section: Thin Vessel Segmentationmentioning

confidence: 99%

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

Zeng

Liao

Tang

et al. 2018

Computers in Biology and Medicine

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This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

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Extraction of vessel networks based on multiview projection and phase field model

Cited by 11 publications

References 29 publications

Vascular Extraction Using MRA Statistics and Gradient Information

Vascular Extraction Using MRA Statistics and Gradient Information

Intracranial Vascular Structure Extraction: A Machine Learning Approach

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

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