Computer Aided Detection And Segmentation Of Intracranial Aneurysms In Ct Angiography

Download or read book Computer Aided Detection and Segmentation of Intracranial Aneurysms in CT Angiography written by Alireza Nikravanshalmani and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This research proposes a computer aided detection (CAD) and segmentation (CAS) of intracranial aneurysm in computer tomography angiography (CTA). The efficiency of the CAD/CAS system is boosted by pre-processing the input image with non-linear diffusion to smooth the CTA data while preserving the edges. A 3D region growing-based approach is used to extract the cerebral arteries followed by entropy-based search space reduction to retain the volume of the circle of Willis (CoW) and the proximal cerebral arteries where nearly all intracranial aneurysms are located, whilst eliminating the extracranial and very distal intracranial circulation. Because cerebral aneurysms vary in size we regard the problem of cerebral aneurysm detection as an intrinsically multi-scale problem and employ a multi-scale approach to all detection analysis. Shape index analysis is employed to determine potential aneurysmal regions (PARs). Hessian analysis and gradient vector field analysis which reveal 3D local shape information are used to further characterise the initial PARs. False positive reduction is then performed based on the analysis of the shape characterisations of the PARs. A ranking score is defined based on the outcomes of the shape analysis to rank the likelihood of PARs. The system allows user to navigate through the ranked PARs and select a candidate aneurysm for further analysis (CAS). The boundary of the selected aneurysm and its parent artery is delineated by using a 3D conditional morphology-based region growing approach. The output is presented to the user to be assessed for the aneurysm orientation relative to the parent vessel. A semi-automatic process is applied to detach the aneurysm from its parent artery. To have a fine segmentation of aneurysm which can be used for characterization of the aneurysm, a 3D geodesic active contour implemented in a level set framework is applied. The volume of the separated aneurysm is quantified as a typical characterization of the aneurysm. The system has been validated on a clinical dataset of 62 eTA scans with average 274 slices per scan (involving 17,028 CT slices) containing 70 aneurysms. Sizes of aneurysms XI I! .I' I lary between 3-16 mm. 42 CTA scans have been used as training dataset for parameter selection and 20 CTA scans have been used as a test dataset. The sensitivity of the system for the CAD component is 97% with the average false positive of 2.24 per dataset (0.008 per slice). CAS performance was evaluated by dual visual judgment of an expert neuroradiologist and neurosurgeon. The detection and segmentation performance indicate the approach has potential in clinical applications.