Outputs (9)

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray (2022)
Presentation / Conference Contribution
Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022, July). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, inclu... Read More about MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Presentation / Conference Contribution
Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021, October). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. Presented at 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada

Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems. Common technologies for... Read More about Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.

Segmentation of macular edema datasets with small residual 3D U-Net architectures (2020)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020, October). Segmentation of macular edema datasets with small residual 3D U-Net architectures. Presented at 20th IEEE International Conference on BioInformatics and BioEngineering, Cincinnati, OH

This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find t... Read More about Segmentation of macular edema datasets with small residual 3D U-Net architectures.

The relationship between curvilinear structure enhancement and ridge detection approaches (2020)
Journal Article
Alhasson, H., Willcocks, C. G., Alharbi, S. S., Kasim, A., & Obara, B. (2021). The relationship between curvilinear structure enhancement and ridge detection approaches. Visual Computer, 37(8), 2263-2283. https://doi.org/10.1007/s00371-020-01985-4

Curvilinear structure detection and quantification is a large research area with many imaging applications in fields such as biology, medicine, and engineering. Curvilinear enhancement is often used as a pre-processing stage for ridge detection, but... Read More about The relationship between curvilinear structure enhancement and ridge detection approaches.

Sequential graph-based extraction of curvilinear structures (2019)
Journal Article
Alharbi, S. S., Willcocks, C., Jackson, P. T., Alhasson, H. F., & Obara, B. (2019). Sequential graph-based extraction of curvilinear structures. Signal, Image and Video Processing, 13(5), 941-949. https://doi.org/10.1007/s11760-019-01431-6

In this paper, a new approach is proposed to extract an ordered sequence of curvilinear structures in images, capturing the largest and most influential paths first and then progressively extracting smaller paths until a prespecified size is reached.... Read More about Sequential graph-based extraction of curvilinear structures.

TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text (2018)
Presentation / Conference Contribution
Medhat, F., Mohammadi, M., Jaf, S., Willcocks, C., Breckon, T., Matthews, P., McGough, A. S., Theodoropoulos, G., & Obara, B. (2018, December). TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

—Text recognition of scanned documents is usually dependent upon the type of text, being handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be... Read More about TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text.

Interactive GPU Active Contours for Segmenting Inhomogeneous Objects (2017)
Journal Article
Willcocks, C. G., Jackson, P. T., Nelson, C. J., Nasrulloh, A., & Obara, B. (2019). Interactive GPU Active Contours for Segmenting Inhomogeneous Objects. Journal of Real-Time Image Processing, 16(6), 2305-2318. https://doi.org/10.1007/s11554-017-0740-1

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU im... Read More about Interactive GPU Active Contours for Segmenting Inhomogeneous Objects.

Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes (2017)
Journal Article
Nasrulloh, A., Willcocks, C., Jackson, P., Geenen, C., Habib, M., Steel, D., & Obara, B. (2018). Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes. IEEE Transactions on Medical Imaging, 37(2), 580-589. https://doi.org/10.1109/tmi.2017.2767908

Macular holes are blinding conditions where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables including the macular hole size and shape. High-r... Read More about Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes.

Extracting 3D parametric curves from 2D images of helical objects (2016)
Journal Article
Willcocks, C., Jackson, P. T., Nelson, C. J., & Obara, B. (2016). Extracting 3D parametric curves from 2D images of helical objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1757-1769. https://doi.org/10.1109/tpami.2016.2613866

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosi... Read More about Extracting 3D parametric curves from 2D images of helical objects.