906 research outputs found

    The role of optical and virt ual colonoscopy in colorectal neoplasms

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    Purpose: High prevalence of colon carcinoma explains the continued high mortality rate of this disease.Utilizing a strategy of virtual colonoscopy (VC) in patients aged over 50 years with optical colonoscopy (OC) following-up for removal of detected adenomatous polyps may result in lowering the colon cancer death rate. However, VC diagnostic potential has not been widely recognized yet.Material and methods: This article reviews the currently available in diagnostic options in colorectal neoplasms and discusses their advantages and drawbacks.Results: VC has many advantages over the existing options and its several drawbacks can be mitigated so that it would become a valuable diagnostic modality. A strategy that utilizes VC for screening of patients over the age of 50 years and OC for screening high-risk individuals and those with positive VC findings would result in a significantly reduced colon cancer mortality rate.Conclusion: Both OC and VC (i.e., CTC and MRC) progress toward the clinical needs as new technologies are developed and applied to overcome the drawbacks of these diagnostic methods. Each of them plays a unique role for colon cancer prevention

    Enhanced computer assisted detection of polyps in CT colonography

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    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

    Virtual colon unfolding for polyp detection

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    Master'sMASTER OF ENGINEERIN

    Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography

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    Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted

    Registration of prone and supine CT colonography images and its clinical application

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    Computed tomographic (CT) colonography is a technique for detecting bowel cancer and potentially precancerous polyps. CT imaging is performed on the cleansed and insufflated bowel in order to produce a virtual endoluminal representation similar to optical colonoscopy. Because fluids and stool can mimic pathology, images are acquired with the patient in both prone and supine positions. Radiologists then match endoluminal locations visually between the two acquisitions in order to determine whether pathology is real or not. This process is hindered by the fact that the colon can undergo considerable deformation between acquisitions. Robust and accurate automated registration between prone and supine data acquisitions is therefore pivotal for medical interpretation, but a challenging problem. The method proposed in this thesis reduces the complexity of the registration task of aligning the prone and supine CT colonography acquisitions. This is done by utilising cylindrical representations of the colonic surface which reflect the colon's specific anatomy. Automated alignment in the cylindrical domain is achieved by non-rigid image registration using surface curvatures, applicable even when cases exhibit local luminal collapses. It is furthermore shown that landmark matches for initialisation improve the registration's accuracy and robustness. Additional performance improvements are achieved by symmetric and inverse-consistent registration and iteratively deforming the surface in order to compensate for differences in distension and bowel preparation. Manually identified reference points in human data and fiducial markers in a porcine phantom are used to validate the registration accuracy. The potential clinical impact of the method has been evaluated using data that reflects clinical practise. Furthermore, correspondence between follow-up CT colonography acquisitions is established in order to facilitate the clinical need to investigate polyp growth over time. Accurate registration has the potential to both improve the diagnostic process and decrease the radiologist's interpretation time. Furthermore, its result could be integrated into algorithms for improved computer-aided detection of colonic polyps

    Automatic colonic polyp detection using curvature analysis for standard and low dose CT data

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    Colon cancer is the second leading cause of cancer related deaths in the developed nations. Early detection and removal of colorectal polyps via screening is the most effective way to reduce colorectal cancer (CRC) mortality. Computed Tomography Colonography (CTC) or Virtual Colonoscopy (VC) is a rapidly evolving non-invasive technique and the medical community view this medical procedure as an alternative to the standard colonoscopy for the detection of colonic polyps. In CTC the first step for automatic polyp detection for 3D visualization of the colon structure and automatic polyp detection addresses the segmentation of the colon lumen. The segmentation of colon lumen is far from a trivial task as in practice many datasets are collapsed due to incorrect patient preparation or blockages caused by residual water/materials left in the colon. In this thesis a robust multi-stage technique for automatic segmentation of the colon is proposed tha t maximally uses the anatomical model of a generic colon. In this regard, the colon is reconstructed using volume by length analysis, orientation, length, end points, geometrical position in the volumetric data, and gradient of the centreline of each candidate air region detected in the CT data. The proposed method was validated using a total of 151 standard dose (lOOmAs) and 13 low-dose (13mAs-40mAs) datasets and the collapsed colon surface detection was always higher than 95% with an average of 1.58% extra colonic surface inclusion. The second major step of automated CTC attempts the identification of colorectal polyps. In this thesis a robust method for polyp detection based on surface curvature analysis has been developed and evaluated. The convexity of the segmented colon surface is sampled using the surface normal intersection, Hough transform, 3D histogram, Gaussian distribution, convexity constraint and 3D region growing. For each polyp candidate surface the morphological and statistical features are extracted and the candidate surface is classified as a polyp/fold structure using a Feature Normalized Nearest Neighbourhood classifier. The devised polyp detection scheme entails a low computational overhead (typically takes 3.60 minute per dataset) and shows 100% sensitivity for polyps larger than 10mm, 92% sensitivity for polyps in the range 5 to 10mm and 64.28% sensitivity for polyp smaller than 5mm. The developed technique returns in average 4.01 false positives per dataset. The patient exposure to ionising radiation is the major concern in using CTC as a mass screening technique for colonic polyp detection. A reduction of the radiation dose will increase the level of noise during the acquisition process and as a result the quality of the CT d a ta is degraded. To fully investigate the effect of the low-dose radiation on the performance of automated polyp detection, a phantom has been developed and scanned using different radiation doses. The phantom polyps have realistic shapes (sessile, pedunculated, and flat) and sizes (3 to 20mm) and were designed to closely approximate the real polyps encountered in clinical CT data. Automatic polyp detection shows 100% sensitivity for polyps larger than 10mm and shows 95% sensitivity for polyps in the range 5 to 10mm. The developed method was applied to CT data acquired at radiation doses between 13 to 40mAs and the experimental results indicate th a t robust polyp detection can be obtained even at radiation doses as low as 13mAs

    Facilitating Colorectal Cancer Diagnosis with Computed Tomographic Colonography

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    Computed tomographic colonography (CTC) is a diagnostic technique involving helical volume acquisition of the cleansed, distended colorectum to detect colorectal cancer or potentially premalignant polyps. This Thesis summarises the evidence base, identifies areas in need of further research, quantifies sources of bias and presents novel techniques to facilitate colorectal cancer diagnosis using CTC. CTC literature is reviewed to justify the rationale for current implementation and to identify fruitful areas for research. This confirms excellent diagnostic performance can be attained providing CTC is interpreted by trained, experienced observers employing state-of-the-art implementation. The technique is superior to barium enema and consequently, it has been embraced by radiologists, clinicians and health policy-makers. Factors influencing generalisability of CTC research are investigated, firstly with a survey of European educational workshop participants which revealed limited CTC experience and training, followed by a systematic review exploring bias in research studies of diagnostic test accuracy which established that studies focussing on these aspects were lacking. Experiments to address these sources of bias are presented, using novel methodology: Conjoint analysis is used to ascertain patients‘ and clinicians’ attitudes to false-positive screening diagnoses, showing that both groups overwhelmingly value sensitivity over specificity. The results inform a weighted statistical analysis for CAD which is applied to the results of two previous studies showing the incremental benefit is significantly higher for novices than experienced readers. We have employed eye-tracking technology to establish the visual search patterns of observers reading CTC, demonstrated feasibility and developed metrics for analysis. We also describe development and validation of computer software to register prone and supine endoluminal surface locations demonstrating accurate matching of corresponding points when applied to a phantom and a generalisable, publically available, CTC database. Finally, areas in need of future development are suggested

    Application of diffusion techniques to the segmentation of Mr 3D images for virtual colonoscopy

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    Master'sMASTER OF ENGINEERIN

    Multi-scale and multi-spectral shape analysis: from 2d to 3d

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    Shape analysis is a fundamental aspect of many problems in computer graphics and computer vision, including shape matching, shape registration, object recognition and classification. Since the SIFT achieves excellent matching results in 2D image domain, it inspires us to convert the 3D shape analysis to 2D image analysis using geometric maps. However, the major disadvantage of geometric maps is that it introduces inevitable, large distortions when mapping large, complex and topologically complicated surfaces to a canonical domain. It is demanded for the researchers to construct the scale space directly on the 3D shape. To address these research issues, in this dissertation, in order to find the multiscale processing for the 3D shape, we start with shape vector image diffusion framework using the geometric mapping. Subsequently, we investigate the shape spectrum field by introducing the implementation and application of Laplacian shape spectrum. In order to construct the scale space on 3D shape directly, we present a novel idea to solve the diffusion equation using the manifold harmonics in the spectral point of view. Not only confined on the mesh, by using the point-based manifold harmonics, we rigorously derive our solution from the diffusion equation which is the essential of the scale space processing on the manifold. Built upon the point-based manifold harmonics transform, we generalize the diffusion function directly on the point clouds to create the scale space. In virtue of the multiscale structure from the scale space, we can detect the feature points and construct the descriptor based on the local neighborhood. As a result, multiscale shape analysis directly on the 3D shape can be achieved
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