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A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly. [ABSTRACT FROM AUTHOR]

The analysis of cerebrovascular shape is important for the diagnose and pathologic identification. But as the limitation of the segmentation algorithm, the complete cerebrovascular volume data are difficult to obtain. So the triangle mesh of the vessel model generated for the medical images may appear many gaps. In the paper, we present a extension algorithm for Ball B-Spline curve with G 2 continuity to repair the cerebrovascular structure from time-of-flight (TOF) magnetic resonance angiography (MRA) data. Ball B-Spline curve has its distinct advantages in representing a 3D tube like organs. A ball Bezier segment is used to construct the extending part and G 2 -continuity is applied to describe the smoothness at the joints. Fairness of the extending ball Bezier curve segment is achieved by minimizing energy objective functions for the center curve and the radius function separately. New control balls are computed by unclamping algorithm to represent the whole extended ball B-Spline curve. The experimental results demonstrate the effectiveness of our algorithm. The final results show that the proposed method provides good blending result, especially for those blood vessels of small size. [ABSTRACT FROM AUTHOR]

We present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes. [ABSTRACT FROM AUTHOR]

The precise segmentation of cerebral vessels is essential for detecting cerebral diseases. This work proposed a novel method for automatic extraction of blood vessels. The proposed framework includes three steps: (a) projection from 3D volume to 2D plane: in order to make up the small percentage vessels occupy in each slice and to avoid overlapping between vessels, the volume dataset is projected from different directions; (b) extraction on 2D plane: a new energy model is proposed using phase-field and statistical information which is based on the Allen–Cahn equation with a double well potential and statistical data fitting terms. The segmentation is based on curve evolution. This model is effective in extracting blood vessels with low contrast, multi-branch structure and intensity inhomogeneity from projection images; (c) projecting back from 2D plane to 3D volume: the pixels segmented from previous step will be projected back into the volume dataset, and the corresponding voxels in the volume will be reserved to construct the blood vessels in three-dimensional space. Experimental results illustrate that the performance of the methods is better than existing techniques. [ABSTRACT FROM AUTHOR]

Abstract: Angiograms have been extensively used by neurosurgeons for vascular and non-vascular pathology. Indeed, examining the cerebral vessel network is helpful in revealing arteriosclerosis, diabetes, hypertension, cerebrovascular diseases and strokes. Thus, accurate segmentation of blood vessels in the brain is of major importance to radiologists. Many algorithms have been proposed for blood vessel segmentation. Although they work well for segmenting major parts of vessels, these techniques cannot handle challenging problems including (a) segmentation of thinner blood vessels due to low contrast around thin blood vessels; (b) inhomogeneous intensities, which lead to inaccurate segmentation. In order to tackle these challenges, we developed a new Allen Cahn (AC) equation and likelihood model to segment blood vessels in angiograms. Its level set formulation combines length, region-based and regularization terms. The length term is represented by the AC equation with a double well potential. The region-based term combines both local and global statistical information, where the local part deals with the intensity inhomogeneity, and the global part solves the low contrast problem. Finally, the regularization term ensures the stability of contour evolution. Experimental results show that the proposed method is both efficient and robust, and is able to segment inhomogeneous images with an arbitrary initial contour. It outperforms other methods in detecting finer detail. [Copyright &y& Elsevier]