Texture Analysis of CT Images for Vascular Segmentation: A Revised Run Length Approach
In this paper we present a textural feature analysis applied to a medical image segmentation problem where other methods fail, i.e. the localization of thrombotic tissue in the aorta. This problem is extremely relevant because many clinical applications are being developed for the computer assisted, image driven planning of vascular intervention, but standard segmentation techniques based on edges or gray level thresholding are not able to differentiate thrombus from surrounding tissues like vena, pancreas having similar HU average and noisy patterns [3,4]. Our work consisted in a deep analysis of the texture segmentation approaches used for CT scans, and on experimental tests performed to find out textural features that better discriminate between thrombus and other tissues. Found that some Run Length codes perform well both in literature and experiments, we tried to understand the reason of their success suggesting a revision of this approach with feature selection and the use of specifically thresholded Run Lengths that improves the discriminative power of measures reducing the computational cost.
- 1.de Bruijne, M., et al.: Active shape model based segmentation of abdominal aortic aneurysms in CTA images. In: Proc. SPIE Medical Imaging 2002: Image Proc., vol. 4684, pp. 463–474 (2002)Google Scholar
- 2.Subasic, M., Loncaric, S., Sorantin, E.: 3-D image analysis of abdominal aortic aneurysm. In: Proc. SPIE Medical Imaging, vol. 4684, pp. 1681–1689 (2002)Google Scholar
- 8.Xu, D.H., et al.: Run Lenght Encoding for Volumetric Texture. In: Proc. of the 4th IASTED Int. Conf. on Visualization, Imaging, and Image Processing (2004)Google Scholar
- 9.Ito, M., et al.: Trabecular texture analysis of CT images in the relationship with spinal fracture. Radiology 194(1), 55–59 (1995)Google Scholar
- 11.Koss, J.E., et al.: Abdominal Organ Segmentation Using Texture Transforms and a Hopfield Neural Network. IEEE Trans. on Medical Imaging 18(7) (1999)Google Scholar
- 12.Raicu, D.S., et al.: A Texture Dictionary for Human Organs Tissues’ Classification. In: Proc. 8th World Multiconf. on Syst., Cyb. and Informatics, Orlando, USA (2004)Google Scholar