Colon centreline calculation for CT colonography using optimised 3D opological thinning

CT colonography is an emerging technique for colorectal cancer screening. This technique facilitates noninvasive imaging of the colon interior by generating virtual reality models of the colon lumen. Manual navigation through these models is a slow and tedious process. It is possible to automate navigation by calculating the centreline of the colon lumen. There are numerous well documented approaches for centreline calculation. Many of these techniques have been developed as alternatives to 3D topological thinning which has been discounted by others due to its computationally intensive nature. This paper describes a fully automated, optimised version of 3D topological thinning that has been specifically developed for calculating the centreline of the human colon

Fast colon centreline calculation using optimised 3D topological thinning

Topological thinning can be used to accurately identify the central path through a computer model of the colon generated using computed tomography colonography. The central path can subsequently be used to simplify the task of navigation within the colon model. Unfortunately standard topological thinning is an extremely inefficient process. We present an optimised version of topological thinning that significantly improves the performance of centreline calculation without compromising the accuracy of the result. This is achieved by using lookup tables to reduce the computational burden associated with the thinning process

Vascular Modeling from Volumetric Diagnostic Data: A Review

Reconstruction of vascular trees from digital diagnostic images is a challenging task in the development of tools for simulation and procedural planning for clinical use. Improvements in quality and resolution of acquisition modalities are constantly increasing the fields of application of computer assisted techniques for vascular modeling and a lot of Computer Vision and Computer Graphics research groups are currently active in the field, developing methodologies, algorithms and software prototypes able to recover models of branches of human vascular system from different kinds of input images. Reconstruction methods can be extremely different according to image type, accuracy requirements and level of automation. Some technologies have been validated and are available on medical workstation, others have still to be validated in clinical environments. It is difficult, therefore, to give a complete overview of the different approach used and results obtained, this paper just presents a short review including some examples of the principal reconstruction approaches proposed for vascular reconstruction, showing also the contribution given to the field by the Medical Application Area of CRS4, where methods to recover vascular models have been implemented and used for blood flow analysis, quantitative diagnosis and surgical planning tools based on Virtual Reality

Enhanced computer assisted detection of polyps in CT colonography

This thesis presents a novel technique for automatically detecting colorectal polyps in computed tomography colonography (CTC). The objective of the documented computer assisted diagnosis (CAD) technique is to deal with the issue of false positive detections without adversely affecting polyp detection sensitivity. The thesis begins with an overview of CTC and a review of the associated research areas, with particular attention given to CAD-CTC. This review identifies excessive false positive detections as a common problem associated with current CAD-CTC techniques. Addressing this problem constitutes the major contribution of this thesis. The documented CAD-CTC technique is trained with, and evaluated using, a series of clinical CTC data sets These data sets contain polyps with a range of different sizes and morphologies. The results presented m this thesis indicate the validity of the developed CAD-CTC technique and demonstrate its effectiveness m accurately detecting colorectal polyps while significantly reducing the number of false positive detections