93 research outputs found
Enhanced algorithms for lesion detection and recognition in ultrasound breast images
Mammography is the gold standard for breast cancer detection. However, it has very
high false positive rates and is based on ionizing radiation. This has led to interest in
using multi-modal approaches. One modality is diagnostic ultrasound, which is based
on non-ionizing radiation and picks up many of the cancers that are generally missed
by mammography. However, the presence of speckle noise in ultrasound images has a
negative effect on image interpretation. Noise reduction, inconsistencies in capture
and segmentation of lesions still remain challenging open research problems in
ultrasound images.
The target of the proposed research is to enhance the state-of-art computer vision
algorithms used in ultrasound imaging and to investigate the role of computer
processed images in human diagnostic performance. [Continues.
Objective Classes for Micro-Facial Expression Recognition
Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during machine learning training. We will show that classifying expressions
using Action Units, instead of predicted emotion, removes the potential bias of
human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D
feature descriptors. The experiments are evaluated on two benchmark FACS coded
datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when
classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the
result of the state-of-the-art 5-class emotional-based classification in CASME
II. Results indicate that classification based on Action Units provides an
objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for
journal revie
Grid-enabled mammographic auditing and training system
Effective use of new technologies to support healthcare initiatives is important and current research is moving towards
implementing secure grid-enabled healthcare provision. In the UK, a large-scale collaborative research project (GIMI:
Generic Infrastructures for Medical Informatics), which is concerned with the development of a secure IT infrastructure
to support very widespread medical research across the country, is underway. In the UK, there are some 109 breast
screening centers and a growing number of individuals (circa 650) nationally performing approximately 1.5 million
screening examinations per year. At the same, there is a serious, and ongoing, national workforce issue in screening
which has seen a loss of consultant mammographers and a growth in specially trained technologists and other nonradiologists.
Thus there is a need to offer effective and efficient mammographic training so as to maintain high levels of
screening skills. Consequently, a grid based system has been proposed which has the benefit of offering very large
volumes of training cases that the mammographers can access anytime and anywhere. A database, spread geographically
across three university systems, of screening cases is used as a test set of known cases. The GIMI mammography
training system first audits these cases to ensure that they are appropriately described and annotated. Subsequently, the
cases are utilized for training in a grid-based system which has been developed. This paper briefly reviews the
background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations,
and future plans of such a grid based approach
Facial Behavioral Analysis: A Case Study in Deception Detection
YesThe objective of every wind energy producer is to reduce operational costs associated to the production as a way to increase profits. One other issue that must be looked carefully is the equipment maintenance. Increase the availability of wind turbines by reducing the downtime associated to failures is a good strategy to achieve the main goal of increase profits.
As a way to help in the definition of the best maintenance strategies, condition monitoring systems (CMS) have an important role to play. Informatics tools to make the condition monitoring of the wind turbines were developed and are now being installed as a way to help producers reducing the operational costs. There are a lot of developed systems to do the monitoring of a wind turbine or the whole wind park, in this paper will be made an overview of the most important systems
Individualised grid-enabled mammographic training system
The PERFORMS self-assessment scheme measures individuals skills in identifying key mammographic features on sets
of known cases. One aspect of this is that it allows radiologists’ skills to be trained, based on their data from this scheme.
Consequently, a new strategy is introduced to provide revision training based on mammographic features that the
radiologist has had difficulty with in these sets. To do this requires a lot of random cases to provide dynamic, unique,
and up-to-date training modules for each individual. We propose GIMI (Generic Infrastructure in Medical Informatics)
middleware as the solution to harvest cases from distributed grid servers. The GIMI middleware enables existing and
legacy data to support healthcare delivery, research, and training. It is technology-agnostic, data-agnostic, and has a
security policy. The trainee examines each case, indicating the location of regions of interest, and completes an
evaluation form, to determine mammographic feature labelling, diagnosis, and decisions. For feedback, the trainee can
choose to have immediate feedback after examining each case or batch feedback after examining a number of cases. All
the trainees’ result are recorded in a database which also contains their trainee profile. A full report can be prepared for
the trainee after they have completed their training. This project demonstrates the practicality of a grid-based
individualised training strategy and the efficacy in generating dynamic training modules within the coverage/outreach of
the GIMI middleware. The advantages and limitations of the approach are discussed together with future plans
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