Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images.

Abstract

Cerebrovascular segmentation plays an important role in medical diagnosis. This study was conducted to develop a threshold segmentation algorithm for automatic extraction and volumetric quantification of cerebral vessels on brain magnetic resonance angiography (MRA) images. The MRA images of 10 individuals were acquired using a 3 Tesla MR scanner (Intera-achieva SMI-2.1, Philips Medical Systems). Otsu's method was used to divide the brain MRA images into two parts, namely, foreground and background regions. To extract the cerebral vessels, we performed the threshold segmentation algorithm on the foreground region by comparing two different statistical distributions. Automatically segmented vessels were compared with manually segmented vessels. Different similarity metrics were used to assess the changes in segmentation performance as a function of a weighted parameter w used in segmentation algorithm. Varying w from 2 to 100 resulted in a false positive rate ranging from 117% to 3.21%, and a false negative rate ranging from 8.23% to 28.97%. The Dice similarity coefficient (DSC), which reflected the segmentation accuracy, initially increased and then decreased as w increased. The suggested range of values for w is [10, 20] given that the maximum DSC (e.g., DSC=0.84) was obtained within this range. The performance of our method was validated by comparing with manual segmentation. The proposed threshold segmentation method can be used to accurately and efficiently extract cerebral vessels from brain MRA images. Threshold segmentation may be used for studies focusing on three-dimensional visualization and volumetric quantification of cerebral vessels.