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Statistical cerebrovascular segmentation in three‐dimensional rotational angiography based on maximum intensity projections
Author(s) -
Gan Rui,
Wong Wilbur C. K.,
Chung Albert C. S.
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2001820
Subject(s) - segmentation , thresholding , artificial intelligence , maximum intensity projection , computer science , image segmentation , computer vision , medical imaging , scale space segmentation , pattern recognition (psychology) , expectation–maximization algorithm , projection (relational algebra) , region growing , intensity (physics) , statistical model , angiography , mathematics , image (mathematics) , algorithm , radiology , statistics , medicine , physics , maximum likelihood , optics
Segmentation of three‐dimensional rotational angiography (3D‐RA) can provide quantitative 3D morphological information of vasculature. The expectation maximization‐(EM‐) based segmentation techniques have been widely used in the medical image processing community, because of the implementation simplicity, and computational efficiency of the approach. In a brain 3D‐RA, vascular regions usually occupy a very small proportion (around 1%) inside an entire image volume. This severe imbalance between the intensity distributions of vessels and background can lead to inaccurate statistical modeling in the EM‐based segmentation methods, and thus adversely affect the segmentation quality for 3D‐RA. In this paper we present a new method for the extraction of vasculature in 3D‐RA images. The new method is fully automatic and computationally efficient. As compared with the original 3D‐RA volume, there is a larger proportion (around 20%) of vessels in its corresponding maximum intensity projection (MIP) image. The proposed method exploits this property to increase the accuracy of statistical modeling with the EM algorithm. The algorithm takes an iterative approach to compiling the 3D vascular segmentation progressively with the segmentation of MIP images along the three principal axes, and use a winner‐takes‐all strategy to combine the results obtained along individual axes. Experimental results on 12 3D‐RA clinical datasets indicate that the segmentations obtained by the new method exhibit a high degree of agreement to the ground truth segmentations and are comparable to those produced by the manual optimal global thresholding method.