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
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
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.