Biomedical subjects

Paul F Whelan

Publications and source records attributed to Paul F Whelan.

7 recordsLinked to original sources

PubMed2006

Development of a synthetic phantom for the selection of optimal scanning parameters in CAD-CT colonography.

The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning parameters including slice thickness, reconstruction interval, field of view, table speed and radiation dose on the overall performance of a computer aided detection (CAD)-CTC system. From these parameters the radiation dose received a special attention, as the major problem associated with CTC is the patient exposure to significant levels of ionising radiation. To examine the influence of the scanning parameters we performed 51 CT scans where the spread of scanning parameters was divided into seven different protocols. A large number of experimental tests were performed and the results analysed. The results show that automatic polyp detection is feasible even in cases when the CAD-CTC system was applied to low dose CT data acquired with the following protocol: 13 mAs/rotation with collimation of 1.5 mm x 16 mm, slice thickness of 3.0mm, reconstruction interval of 1.5 mm, table speed of 30 mm per rotation. The CT phantom data acquired using this protocol was analysed by an automated CAD-CTC system and the experimental results indicate that our system identified all clinically significant polyps (i.e. larger than 5 mm).

Colonography, Computed Tomographic↗

PubMed2006

The use of 3D surface fitting for robust polyp detection and classification in CT colonography.

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20min per dataset) and shows 100% sensitivity for phantom polyps greater than 5mm. It also shows 100% sensitivity for real polyps larger than 10mm and 91.67% sensitivity for polyps between 5 to 10mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.

Algorithms↗

PubMed2005

MRI diffusion-based filtering: a note on performance characterisation.

Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality.

Algorithms↗

PubMed2005

Rapid automated measurement of body fat distribution from whole-body MRI.

OBJECTIVE: The purpose of this article is to determine the feasibility of using computer-assisted diagnosis (CAD) techniques to automatically identify, localize, and measure body fat tissue from a rapid whole-body MRI examination. CONCLUSION: Whole-body MRI in conjunction with CAD allows a fast, automatic, and accurate approach to body fat measurement and localization and can be a useful alternative to body mass index. Whole-body fat analysis can be achieved in less than 5 min.

Adipose Tissue↗

PubMed2004

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.

Colonography, Computed Tomographic↗

PubMedDate not supplied

Informatics in radiology (infoRAD): portable toolkit for providing straightforward access to medical image data.

Computer-aided analysis of medical images usually involves the development of custom software applications that interpret, process, and ultimately display medical image data. The interpretation stage involves decoding the image data and presenting them to the application developer for further processing. A toolkit has been created specifically for interpreting medical image data; it thus acts as a platform for development of medical imaging applications. The toolkit, which is referred to as NeatMed, is intended to reduce development time by eliminating the need for the application developer to deal directly with medical image data. NeatMed was implemented by using Java, a programming language with a range of attractive features including ease of use, extensive support material, and portability. NeatMed was developed specifically for use in a research environment. Straightforward to use and well documented, it is intended as an alternative to commercially available medical imaging toolkits. NeatMed currently provides support for the Digital Imaging and Communications in Medicine and Analyze medical image file formats. Support material including sample source code is available via the Internet; links to related resources are also provided. Most important, NeatMed is freely available and its continuing development is motivated by requests and suggestions from end users.

Confidentiality↗

PubMedDate not supplied

Informatics in radiology (infoRAD): NeatVision: visual programming for computer-aided diagnostic applications.

A free visual programming-based image analysis development environment for medical imaging applications called NeatVision was developed to provide high-level access to a wide range of image processing algorithms through a well-defined, easy-to-use graphical interface. The system contains over 300 image manipulation, processing, and analysis algorithms. For more advanced users, an upgrade path is provided to extend the core library with use of the developer's interface, giving users access to additional plug-in features, automatic source code generation, compilation with full error feedback, and dynamic algorithm updates. NeatVision was designed to allow users at all levels of expertise to focus on the computer vision design task for computer-aided diagnostic (CAD) applications rather than the subtleties of a particular programming language. The environment allows the designers of image analysis-based CAD techniques to implement their ideas in a dynamic and straightforward manner. Both NeatVision standard and developer's versions can be downloaded free of charge from the Internet and can run on a variety of computer platforms.

Diagnosis, Computer-Assisted↗