James Shuttleworth

Colour texture analysis using co-occurrence matrices for classification of colon cancer images

Worldwide, colorectal cancer is the third most common malignant neoplasm. Automated classification of cytological images of colon tissue samples has been investigated, but diagnosis in all cases still requires human judgement. With the large numbers of cases of colon cancer each year, the workload placed on pathologists is immense. Texture is a powerful discriminating metric and the use of grey-level texture for classification of colon images has been extensively researched. One common technique is the extraction of texture metrics from grey-level co-occurrence matrices. However, using grey-scale images discards information contained in the differences of hue and saturation that may provide further classification information. We present the findings of an investigation of the discriminating ability of colour texture using co-occurrence matrices. Comparisons are made between grey-scale and colour texture analysis. Using statistical analysis, we show that classification using colour texture offers an improvement over classification based solely on grey-level texture.

Experimental Applications of Mapping Services in Wireless Sensor Networks

Wireless sensor networks typically gather data at a number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than points of data. This paper examines one way in which this can be done. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. We present an implementation of this service and discuss its merits and shortcomings. Additionally, we present an initial application of the service in the form of isopleth generation. That is, the delineation of contours of constant parameter value. Finally, we discuss the improvements required to create more sophisticated applications and services and examine the benefits these improvements would bring.

In at the Deep End: An Activity-Led Introduction to First Year Creative Computing

Misconceptions about the nature of the computing disciplines pose a serious problem to university faculties that offer computing degrees, as students enrolling on their programmes may come to realise that their expectations are not met by reality. This frequently results in the students’ early disengagement from the subject of their degrees which in turn can lead to excessive ‘wastage’, that is, reduced retention. In this paper, we report on our academic group’s attempts within creative computing degrees at a UK university to counter these problems through the introduction of a 6 week long project that newly enrolled students embark on at the very beginning of their studies. This group project, involving the creation of a 3D etch‐a‐sketch‐like computer graphics application with a hardware interface, provides a breadth‐first, activity‐led introduction to the students’ chosen academic discipline, aiming to increase student engagement while providing a stimulating learning experience with the overall goal to increase retention. We present the methods and results of two iterations of these projects in the 2009/2010 and 2010/2011 academic years, and conclude that the approach worked well for these cohorts, with students expressing increased interest in their chosen discipline, in addition to noticeable improvements in retention following the first year of the students’ studies.

Multiresolution colour texture analysis for classifying colon cancer images

Co-occurrence matrices are commonly used to extract fine texture information from images, and have been found to be a useful tool for measuring dysplasia in histological images of the colon. Pathologists, however, measure dysplasia in tissue samples at structural as well as cytological levels. We present our findings after investigating modifications to the cooccurrence matrix technique to measure this low frequency colour texture information for the classification of colon cancer images.

FieldMAP: A Spatiotemporal Field Monitoring Application Prototyping Framework

The fundamental aim of monitoring is to identify abnormalities in the observed phenomena and allow inference of the likely cause. Faced with the common problems of spatially irregular sensor distribution and intermittent sensor measurement availability, key to fulfilling the monitoring aim is filling in the spatiotemporal gaps in the data. While wireless sensor networks (WSNs) technology, combined with microelectromechanical systems availability potentially offer sensing solutions for a variety of application domains, in the context of monitoring applications a conceptual shift is needed from currently available, point-measurement-based ldquosense-and-sendrdquo systems toward the provision of phenomena field representations, in real time, enabling effective visualization of the spatiotemporal patterns. This paper argues the case for a generic, rapid prototyping framework for end-to-end sensing systems that support the approach of providing field representations for visualization. A formal approach to framework development was taken, ensuring that resulting instrumentation systems are well specified. Both the framework development and its evaluation are linked to the full cycle of requirements setting, design, and deployment of a prototype instrumentation system for aerospace applications-specifically, health monitoring of a gas turbine engine. The field monitoring application prototyping (FieldMAP) framework supports multimodal sensing, provides a number of opportunities for data processing and information extraction, caters for monitoring of the instrumentation health, offers a modular field-mapping design component and allows for real-time phenomena visualization, data and information logging, and postanalysis. Experience with the FieldMAP has shown that sophisticated and robust prototypes can be developed in a short period of time.

Learning histopathological microscopy

Histopathological tissue analysis by microscopy is a process that is subjective, prone to inter- and intra-observer variation. This, along with the problems associated with verbalising visual elements of the diagnostic process, make learning the skill quite difficult. Training is long and largely relies on an “apprentice” model, where trainees learn the skill by witnessing an expert at work. Here we present the first findings of a longitudinal study of a group of histopathology trainees. By monitoring the progress of the trainees, we hope to be able to provide information that will improve training and assessment. In this paper we discuss the results of early data collection and analysis, from a web-based study of trainee classification accuracy and classification time.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Sense-enabled mixed reality museum exhibitions

During the past few years museums and other cultural heritage institutions have started making use of handheld technologies to provide tourist guides to their visitors. For open-air sites, a number of experimental and commercial applications have been developed based on location-based guides. However, in museum environments static audiovisual guides are the dominant technologies used. In this paper, we present a novel pervasive mixed reality framework to a sensor network capturing ambient noise that can be used to create tangible cultural heritage exhibitions. Localisation of the visitors can be established in a hybrid manner based on machine vision and a wireless sensor network allowing visitors to interact naturally or with the help of sensors. In terms of interface design, a multimodal mixed reality visualisation domain allows for an audio-visual presentation of cultural heritage artefacts.

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

Abstract Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.

Experimental Applications of Hierarchical Mapping Services in Wireless Sensor Networks

The construction of accurate and efficient visualisations of sense data gathered from wireless sensor networks is an open problem. This paper presents a novel solution in which groups of network nodes cooperate to produce local maps which are cached and merged at a sink node, producing a low- resolution map of the global network. The sink node receives periodic map updates from each cluster-head used to refine an up-to-date global map. The global map gives a low cost interface used to target queries for generating detailed maps from a subset of the sensors in the network. Different map resolutions are adequate for different management applications to perform at a desired level. In an energy constrained network, it is preferable to allow the application to control the level of detail and topology coverage of a generated map. This work gives a complete system solution that solves global network data mapping problems through localized and distributed computation. This is achieved by abstracting and simplifying the routing and interpolation services of the network and turning them into services of the network rather than being the result of coordinating the services of individual nodes. This system changes the mechanism of use of certain capabilities in wireless sensor network; the interpolation service is used by the sensing nodes to deal with mapping data and a cluster-based routing algorithm in which the cluster heads are selected as the caches for local maps. This technique performs favourably against similar techniques which do not exploit such services of the network to generate maps.