Pixel profiling for extraction of arteriovenous malformation in 3-D CTA images

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture which can cause severe brain damage or even death. For planing the embolization procedure of an AVM, the knowledge of the accurate location and size of the malformation is of utmost importance. The main purposes of automatic AVM segmentation are: 1) objective and reproducible segmentation; 2) reduction in processing time (saving resources by requiring less manual work). Furthermore, automatic segmentation with accurate AVM (or aneurysm) characterization were deemed helpful in therapeutic decision making concerning treatment modality (surgical or endovascular). Operator-independent accurate sizing of AVM (aneurysm) would allow strict follow-up until the threshold is reached and the patient referred to treatment. We propose in this paper a novel AVM detection method and a blood vessel tree analysis approach using ordered thinning-based skeletonization. The main contributions are: (1) a new method of profile volume calculation to replace the distance labels in ordered skeletonization; (2) an automatic method for AVM detection and extraction, with accurate positioning and malformation size estimation. The main idea in our work is use the structural (anatomical) vessel differences and the inhomogeneities in distribution of pixel gray values to locate and extract the AVM. The algorithm takes a segmentation result as an input to perform AVM delineation. The algorithm determines the AVM region automatically, without any user interaction and independently of the segmentation algorithm used. The proposed approach is validated on brain blood vessel CTA images before and after embolization. The results obtained using the Dice coefficient comparisons, the volume percent error and the AVM center position show high accuracy of our method and indicate potentials for use in surgical planning.