Improved Direct Volume Visualisation of the Coronary Arteries Using Fused Segmented Regions
Mueller, Daniel C., Maeder, Anthony J., & O'Shea, Peter J. (2005) Improved Direct Volume Visualisation of the Coronary Arteries Using Fused Segmented Regions. In Digital Image Computing: Techniques and Applications, 6-8 December 2005, Cairns, Queensland, Australia.
Coronary heart disease was the single largest cause of sudden death in Australia in 2002. Computed tomography angiography (CTA) provides high resolution, high contrast images of the thoracic cavity, and as such has emerged as the imaging modality of choice for diagnosing and planning treatment for coronary heart disease. However, radiologists and cardiac surgeons require tools to easily identify possible stenosis (narrowed or constricted coronary vessels) in such CTA datasets. We present a method which allows users to interactively visualise a specific three-dimensional region of interest (ROI). In our example, segmentation methods are applied to isolate the coronary vessels, which in turn are visually enhanced using various perceptual cues. The segmentation is achieved using a combination of thresholding, region-growing, and morphological operations. The perceptual enhancement is realized by fusing direct volume rendered images using weighting factors determined by the segmented regions. The user can allow for the easy dissemination of relevant information by adjusting 'transfer functions' to control the degree of ROI enhancement. This approach requires only roughly segmented regions of interest, and allows for the 3D visualisation of calcifications within vessels. This proposed method has significant potential for helping to facilitate the efficient treatment for coronary heart disease. Furthermore, it can be implemented at interactive framerates on comparatively cheap, desktop comuting hardware making it readily accessible.
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|Item Type:||Conference Paper|
|Keywords:||direct volume rendering, coronary arteries, 3D texture mapping, ITK|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2005 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||02 Dec 2005|
|Last Modified:||29 Feb 2012 23:12|
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