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Phase unwrapping is an essential algorithmic step in any measurement system or sensor that seeks to determine continuous phase. Instances of such devices are widespread: e.g. image reconstruction in magnetic resonance imaging (MRI), synthetic aperture radar (SAR) by satellite systems, analysis of seismic data in geophysics and optical instrumentation, to name but a few. Without successfully solving the phase unwrapping problem these instruments cannot function.
The topic is well developed and competition among algorithms is fierce. In 2012 alone, some 235 papers, most of which were describing potential new algorithms, were published in the area. But the continuing need for high-speed, automated and robust unwrapping algorithms poses a major limitation on the employability of phase measuring systems.
Working originally within the context of structured light 3D measurement systems, our research has developed new phase image unwrapping algorithms that constitute significance advances in speed, automation and robustness. The work has led to adoption by industry, as well as use in commercial and government research centres around the globe. Our approach since 2010 has been to make these algorithms freely available to end users. Third parties have gone on to translate our algorithms into other languages, widely used numerical software libraries have incorporated the algorithms and there are high profile industrial users.
Professor Kautz and his team have developed two photo manipulation and processing methods (Exposure Fusion and local Laplacian filtering) that are used to produce well-exposed photographs with tuneable local contrast. Both are robust and consistent without requiring any per-image parameter tuning. Due to its reliability and effectiveness, Exposure Fusion is now considered the standard method for blending multiple photographs into a single well-exposed photograph, and is used by a large number of commercial and non-commercial products. Local Laplacian filtering was chosen by Adobe Systems Incorporated to be the default tool for image enhancements in Adobe Lightroom and Adobe Camera Raw. As a result, these methods are now in the hands of hundreds of thousands users, who use them to create and manipulate well-exposed digital photographs.
The key impact of this project, in the form of `proof of concept', has been by influencing the practice of medical professionals (haematologists) at the Transfusion Medicine & Immunohematology section (in the hospital wing) of the Christian Medical College (CMC) Vellore (India). This has been achieved by developing and implementing system software for segmenting (and watermarking) of the nuclei of the White Blood Cells (WBCs) of peripheral blood smear images to overcome the challenge of identifying various pathological conditions. Segmentation of medical images is a highly challenging process, especially when dealing with blood smear images, which are known to have a very complex cell structure. The project has led to a significant improvement in the work process of haematologists at CMC's hospital wing where the output of this research (software system pilot) is being used. This has had an impact on the way smear slides are digitised, archived, and includes the segmentation, analysis, and watermarking of medical images at CMC. Christian Medical College (CMC) and Hospital at Vellore is an educational and pioneering research institute and a tertiary care hospital (which is the CMC's hospital wing), located at Tamil Nadu in Southern India.
The security of data in printing and network environments is an area of increasing concern to individuals, businesses, government organisations and security agencies throughout the world. Mathematical algorithms developed at the School of Mathematics at Cardiff University represent a significant step-change in existing data security techniques. The algorithms enable greater security in automatic document classification and summarisation, information retrieval and image understanding. Hewlett-Packard (HP), the world's leading PC vendor, funded the research underpinning this development and patented the resulting software, with the aim of strengthening its position as the market leader in this sector of the global information technology industry. Hewlett Packard has incorporated the algorithms in a schedule of upgrades to improve the key security features in over ten million of their electronic devices. Accordingly, the impact claimed is mitigating data security risks for HP users and clients and substantial economic gain for the company.
Research at Swansea University in the area of computational electromagnetics has led to better design of aircraft with respect to radar detection and the screening of internal systems from the effect of unwanted electromagnetic field ingress. A key issue was the development of an ability to accommodate electromagnetically large complex bodies having spatially small, but electromagnetically important, features. In addition, procedures for modelling RF threats, including lightning strikes and electromagnetic hazards, were also developed. Such progress has enabled significant improvement in electromagnetic performance of technology produced by BAE Systems reaching across its Advanced Technology Centre and its business units (Military Aircraft and Information, and Naval Ships). This research enabled two-orders-of-magnitude improvement in efficiency of BAE software compared to previously used techniques, significantly reducing design time. These developments were used on major international programmes such as TYPHOON, the Taranis UCAV (unmanned Combat Air Vehicle).
Led from Dundee by Prof Jason Swedlow FRSE, The Open Microscopy Environment (OME) is an international consortium building tools to enable the storage and analysis of biological image data. OME releases Bio-Formats, an image format translation library, and OMERO, software for the visualisation, management and analysis of image data recorded by microscopes and high-content screening systems. OME software is open-source and transforms the way researchers manage the vast amount of image data routinely produced in research laboratories. Glencoe Software is the commercial arm of OME and provides commercial licenses, support, and customisation for OME's software tools to major industrial customers.
UCL's research has led to changes in patient care for men with prostate cancer, through the implementation of less invasive, image-directed treatment and diagnostic strategies, and clinical trials that use these techniques. The use of medical image registration software to deliver high- intensity ultrasound therapy in a targeted manner has been shown to change the treatment plan in half of the patients participating in a clinical study. New biopsy criteria are now used routinely to classify patient risk at University College Hospital, where, since 2009, clinicians have determined the treatment options for more than 741 prostate cancer patients. The scheme has been adopted, by 15 other hospitals in the UK and internationally, where it has become the recommended standard of care, and has been used to treat more than 1,200 patients.
A unique aspect of the signal and image processing research at the University of Central Lancashire (UCLan) lies in exploitation of the synergies between non-destructive evaluation (NDE) of aerostructures in the aerospace manufacturing sector and non-invasive diagnosis (NID) of patients in the medical sector. For the former, through collaborative research with world leading aerospace companies, data processing technologies used in medical NID have been exploited to ensure structural safety of aircraft at reduced time and cost. For the latter, through collaborative research with the UCLan led Europe-wide network which includes top medical research centres and hospitals, sensing technologies used in aerospace NDE have been exploited to create new measurement modalities for quantitative medical diagnosis of major diseases. Furthermore, arising out the cross-sectoral and interdisciplinary research the Tele-immersive Digital Manufacturing facility (TiM) emerges as our vision for the factory of the future which has attracted investments from the world leading digital technology providers and made impacts on one of the most important manufacturing regions in the world.
A biomarker is a measurement or physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions, or survives. Biomarkers can be used to assess changes induced by a therapy or intervention on a clinically meaningful endpoint.
New quantitative image analysis techniques developed at Imperial College have enabled the computation of imaging biomarkers that are now widely used in clinical trials as well as for healthcare diagnostics. This case study illustrates the resulting key impacts including:
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.