Projects per year
Description
Hardware, software and results generated from testing the performance of a bone segmentation algorithm implemented in both hardware and software for segmenting bone in medical images.
The bone segmentation algorithm used was one described by Haas et al. (doi: 10.1088/0031-9155/53/6/017). The algorithm was implemented in full in software and a simplified version was developed for hardware implementation, which was implemented in both hardware and software.
The software algorithms were executed on a 2.6GHz Intel Core i5-3230M CPU, while the hardware system was implemented on a Xilinx Zynq Z7020 device on an Avnet ZedBoard development board.
3D image data in the form of computed tomography (CT) and cone beam CT (CBCT) scans of patients receiving external beam radiotheray for bladder cancer were obtained from Edinburgh Cancer Centre (n=132) and used as the input data.
The time taken to process each image volume and each slice of each image volume was recorded for each of the three implementations.
See README file for further details.
The bone segmentation algorithm used was one described by Haas et al. (doi: 10.1088/0031-9155/53/6/017). The algorithm was implemented in full in software and a simplified version was developed for hardware implementation, which was implemented in both hardware and software.
The software algorithms were executed on a 2.6GHz Intel Core i5-3230M CPU, while the hardware system was implemented on a Xilinx Zynq Z7020 device on an Avnet ZedBoard development board.
3D image data in the form of computed tomography (CT) and cone beam CT (CBCT) scans of patients receiving external beam radiotheray for bladder cancer were obtained from Edinburgh Cancer Centre (n=132) and used as the input data.
The time taken to process each image volume and each slice of each image volume was recorded for each of the three implementations.
See README file for further details.
| Date made available | 10 Aug 2019 |
|---|---|
| Publisher | University of Strathclyde |
| Date of data production | 19 Sept 2018 - 10 Aug 2019 |
Projects
- 1 Finished
-
Medical Devices Doctoral Training Centre Renewal / RS8651
Mullen, A. (Principal Investigator), Black, R. A. (Co-investigator), Burley, G. (Co-investigator), Crockett, L. (Co-investigator), Laurand, N. (Co-investigator) & Thomson, A. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/08 → 31/03/18
Project: Research Studentship - Internally Allocated
Datasets
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Performance of Hardware Accelerated Bone Segmentation with Noise Reduction on 4DCT Images
Robinson, F. (Creator), Crockett, L. H. (Contributor), Stewart, R. (Contributor), Nailon, W. H. (Contributor) & McLaren, D. (Contributor), University of Strathclyde, 11 Jan 2021
DOI: 10.15129/89d99e2a-a2c6-4f82-9626-b849fc93e6c2
Dataset
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Performance of Hardware Accelerated Bone Segmentation on 4DCT Images
Robinson, F. (Creator), Crockett, L. H. (Contributor), Stewart, R. (Contributor), Nailon, W. H. (Contributor) & McLaren, D. (Contributor), University of Strathclyde, 5 Jan 2021
DOI: 10.15129/e46d23a5-227b-4e2b-8a51-0f4bdd17d644
Dataset