Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images
Introduction
Cerebrovascular segmentation (Kirbas and Quek, 2004, Suri et al., 2002) plays an important role in medical diagnosis. This technique is necessary to perform a three-dimensional (3-D) visualization of cerebral vessels to diagnose, quantify, and grade vascular abnormalities, such as stenosis and aneurysm (Farag et al., 2004). Moreover, an accurate extraction of 3-D structures of cerebral vessels helps in planning and performing neurosurgical procedures (Frangi et al., 2001, Passat et al., 2005). 3-D time-of-flight (TOF) magnetic resonance angiography (MRA) is a noninvasive technique for vessel imaging. After cerebral vessels are segmented from 3-D TOF MRA images, maximum intensity projection (MIP) (Sun and Parker, 1999) method is generally utilized to construct a 3-D volumetric visualization of cerebral vessels and assess the size and location of vessels.
Cerebral vessels are difficult to accurately segment because of complex geometric structures and limited spatial resolution and image contrast (Bogunović et al., 2011). Various segmentation methods have been developed to extract cerebral vessels from brain MRA images, and these methods can be divided into two main categories (Kirbas and Quek, 2004, Yan and Kassim, 2006): skeleton-based and non-skeleton-based. In skeleton-based methods (Kirbas and Quek, 2004, Sorantin et al., 2002), the centerlines of vessels are extracted and a vessel tree is generated by connecting these centerlines. A centerline structure is simulated explicitly or implicitly by using vessel modeling methods. However, the results of skeleton-based methods provide incomplete volumetric information of the vessels.
In non-skeleton-based methods, vessels are directly extracted from 3-D MRA images by using deformable models (Chen and Amini, 2004, Farag et al., 2004, Kozerke et al., 1999, Scherl et al., 2007, Yan and Kassim, 2006) or threshold techniques (Chung and Noble, 1999, Chung et al., 2004, Kim and Park, 2005, Wilson and Noble, 1999). For example, a level set method (Adalsteinsson and Sethian, 1995) is commonly used in deformable model approaches that track the interfaces and shapes of vessels. Level set segmentation is implemented by locally minimizing an energy function with a gradient descent algorithm (Cremers et al., 2007). Different forms of improved level set methods (Adalsteinsson and Sethian, 1995, Chen and Amini, 2004, Farag et al., 2004) have been designed for vessel surface segmentation, but the use of these methods is limited by common factors, such as sensitivity to initial value, speed, and algorithm convergence. Furthermore, threshold segmentation methods (Chung et al., 2004, Kim and Park, 2005) have been extensively investigated. In these methods, a threshold is chosen to distinguish vessels from brain tissues by combining reasonable statistical models and local voxel information. The selection of threshold value directly impacts the segmentation performance.
In this paper, a threshold segmentation method was developed to extract cerebral vessels from 3-D TOF MRA images. In general, extreme value theory (De Haan and Ferreira, 2007) can be used to detect outliers of abnormally low or high values, which occur at the tails of specific probability distributions, such as normal distribution. In MRA images, cerebral vessels present signals higher than surrounding brain tissues with intermediate signals. Homogeneous brain tissues excluding the vessels can be represented by normal distribution, and cerebral vessels can be detected using a specific extreme value distribution, namely, the Gumbel distribution (Kotz and Nadarajah, 2000, Roberts, 2000, Wang et al., 2014). To extract the cerebral vessels from brain MRA images, we determine a threshold by comparing the probability density function (PDF) of the two statistical distributions.
This study aimed to design a threshold segmentation algorithm that can be used to extract cerebral vessels from 3-D TOF MRA images. Two statistical distributions were applied to determine a threshold that could be used to distinguish vessels from brain tissues. To evaluate the performance of the proposed threshold segmentation, we investigated the MRA images of 10 individuals and compared automatically segmented vessels with manually segmented vessels.
Section snippets
Subjects and image acquisition
This study was approved by our institutional review board, and a written informed consent was obtained from each patient. Ten individuals (four males and six females) were enrolled in this study and subjected to cerebrovascular segmentation. These individuals aged between 29 and 85 years (mean age = 59.7 years). 3-D TOF MRA images were acquired using a 3 Tesla MR scanner (Intera-achieva SMI-2.1, Philips Medical Systems). The main imaging parameters were listed as follows: repetition time/echo time
Results
The cerebral vessels on the 3-D TOF MRA images were automatically extracted using the proposed threshold segmentation algorithm. A comparison of the cerebral vessels detected using different values of parameter is illustrated in Fig. 5. The 3-D volumetric visualization of the cerebral vessels was reconstructed on the basis of an MIP technique. The FP signals were relatively more in the segmentation results when the parameter was small (e.g., w = 2 or 5). An example of automatic segmentation
Discussion
In this study, a threshold segmentation algorithm was developed to automatically extract cerebral vessels from 3-D TOF MRA images. The performance of our method was validated by comparing with manual segmentation, which was performed by an experienced radiologist. High DSC (>0.7) demonstrated that the proposed segmentation method could be a reliable method which could be used to perform 3-D volumetric visualization and quantification of cerebral vessels.
The threshold segmentation of cerebral
Acknowledgments
This research is supported by National Basic Research Program of China (973 Program, No. 2010CB732506), National Natural Science Foundation of China (no. 81301213), National Natural Science Foundation of China (no. 81000609), National Natural Science Foundation of China (no. 60972110), and Major Program of Social Science Foundation of China (no. 11&ZD174). We thank the radiologists of Shanghai Sixth People's Hospital for providing their clinical images and ground truth data.
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