11 research outputs found

    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

    Computer-aided detection of colonic polyps with level set-based adaptive convolution in volumetric mucosa to advance CT colonography toward a screening modality

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    As a promising second reader of computed tomographic colonography (CTC) screening, the computer-aided detection (CAD) of colonic polyps has earned fast growing research interest. In this paper, we present a CAD scheme to automatically detect colonic polyps in CTC images. First, a thick colon wall representation, ie, a volumetric mucosa (VM) with several voxels wide in general, was segmented from CTC images by a partial-volume image segmentation algorithm. Based on the VM, we employed a level set-based adaptive convolution method for calculating the first- and second-order spatial derivatives more accurately to start the geometric analysis. Furthermore, to emphasize the correspondence among different layers in the VM, we introduced a middle-layer enhanced integration along the image gradient direction inside the VM to improve the operation of extracting the geometric information, like the principal curvatures. Initial polyp candidates (IPCs) were then determined by thresholding the geometric measurements. Based on IPCs, several features were extracted for each IPC, and fed into a support vector machine to reduce false positives (FPs). The final detections were displayed in a commercial system to provide second opinions for radiologists. The CAD scheme was applied to 26 patient CTC studies with 32 confirmed polyps by both optical and virtual colonoscopies. Compared to our previous work, all the polyps can be detected successfully with less FPs. At the 100% by polyp sensitivity, the new method yielded 3.5 FPs/dataset

    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

    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

    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

    Computer-aided detection of polyps in CT colonography

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    Master'sMASTER OF ENGINEERIN

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

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    Master'sMASTER OF ENGINEERIN
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