Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Haptic control of multi-axis robotic systems

Control of tele-operated remote robot’s is nothing new; the public was introduced to this 'new' field in 1986 when the Chernobyl cleanup began. Pictures of weird and wonderful robotic workers pouring concrete or moving rubble flooded the world. Integration of force feedback or 'haptics' to remote robot's is a new development and one that is likely to make a big difference in man-machine interaction. Development of haptic capable tele-operation schema is a challenge. Often platform specific software is developed for one off tasks. This research focussed on the development of an open software platform for haptic control of multiple remote robotic platforms. The software utilises efficient server/client architecture for low data latency, while efficiently performing required kinematic transforms and data manipulation in real time. A description of the algorithm, software interface and hardware is presented in this paper. Preliminary results are encouraging as haptic control has been shown to greatly enhances remote positioning tasks.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

Relative preference-based recommender systems

This study investigates the problem of making recommendations to users, such as recommending a movie. Several novel models are proposed to make accurate recommendations by analyzing both the explicit and implicit data. Experiment results have confirmed improvements over state-of-the-art models.

Data-driven computation of contact dynamics during two-point manipulation of deformable objects

This study represents a preliminary step towards data-driven computation of contact dynamics during manipulation of deformable objects at two points of contact. A modeling approach is proposed that characterizes the individual interaction at both points and the mutual effects of the two interactions on each other via a set of parameters. Both global as well as local coordinate systems are tested for encoding the contact mechanics. Artificial neural networks are trained on simulated data to capture the object behavior. A comparison of test data with the output of the trained system reveals a mean squared error percentage between 1% and 3% for simple interactions.

The nature of data: illusions of reality

The nature of data has changed as human technology has evolved. ‘Natural’ analogue data stimulate our senses, whilst machine produced data provides an intermediary for natural or artificial data to our senses. In the age of the cyborg (a machine with human attributes) and the bionic person (a human with machine attributes), it is possible for data totally alien from ‘natural reality’ to be fed directly to the brain so by-passing the senses. This is a new form of reality, which lends itself to manipulation of a kind never experienced before. The dreams of deceivers may yet be realised on a mass scale.

Visiview : a system for the visualization of multi-dimensional data

Results generated by simulation of computer systems are often presented as a multi-dimensional data set, where the number of dimensions may be greater than 4 if sufficient system parameters are modelled. This paper describes a visualization system intended to assist in understanding the relationship between, and effect upon system behavior of, the different values of the system parameters.

The system is applied to data that cannot be represented using a mesh or isosurface representation, and in general can only be represented as a cloud of points. The use of stereoscopic rendering and rapid interaction with the data are compared with regard to their value in providing insight into the nature of the data.

A number of techniques are implemented for displaying projections of the data set with up to 7 dimensions, and for allowing intuitive manipulation of the remaining dimensions. In this way the effect of changes in one variable in the presence of a number of others can be explored.

The use of these techniques, when applied to data from computer system simulation, results in an intuitive understanding of the effects of the system parameters on system behavior.

The effect of lumbosacral manipulation on corticospinal and spinal reflex excitability on asymptomatic participants

Objective The aim of the study was to examine the effects of a high-velocity, low-amplitude (HVLA) manipulation to the lumbosacral joint on corticospinal excitability, as measured by motor evoked potentials (MEPs) using transcranial magnetic stimulation, and spinal reflex excitability, as measured by the Hoffman reflex (H-reflex).

Methods In a randomized, controlled, crossover design, 14 asymptomatic volunteers (mean age, 23 ± 5.4 years; 10 men; 4 women) were measured for MEPs and H-reflexes immediately before and after a randomly allocated intervention. The interventions consisted of HVLA applied bilaterally to the lumbosacral joint and a control intervention. Participants returned a week later, and the same procedures were performed using the other intervention. Data for H-reflex and MEP amplitudes were normalized to the M-wave maximum amplitude and analyzed using 2-way analysis of variance with repeated measures.

Results A significant interaction of treatment by time was found for MEP (F1,13 = 4.87, P = .04), and post hoc analyses showed that the MEP/M-wave maximum ratio decreased significantly in the HVLA treatment (P = .02; effect size, 0.68). For H-reflex, there was a significant effect of time (F1,13 = 8.186, P = .01) and treatment and time interaction (F1,13 = 9.05, P = .01), with post hoc analyses showing that H-reflexes were significantly reduced after the HVLA manipulation (P = .004; effect size, 0.94). There were no significant changes in MEP latency or silent period duration.

Conclusion An HVLA manipulation applied to the lumbosacral joint produced a significant decrease in corticospinal and spinal reflex excitability, and no significant change occurred after the control intervention. The changes in H-reflexes were larger than those in MEPs, suggesting a greater degree of inhibition at the level of the spinal cord.

Do leatherback turtles Dermochelys coriacea forage during the breeding season? A combination of data-logging devices provide new insights

Animals which undertake migrations from foraging grounds to suitable breeding areas must adopt strategies in these new conditions in order to minimise the rate at which body condition deteriorates (which will occur due to oogenesis or provisioning for young). For some animals this involves continuing foraging, whereas for others the optimal strategy is to fast during the breeding season. The leatherback turtle undertakes long-distance migrations from temperate zones to tropical breeding areas, and in some of these areas it has been shown to exhibit diving behaviour indicative of foraging. We used conventional time–depth recorders and a single novel mouth-opening sensor to investigate the foraging behaviour of leatherback turtles in the southern Caribbean. Diving behaviour suggested attempted foraging on vertically migrating prey with significantly more diving to a more consistent depth occurring during the night. No obvious prey manipulation was detected by the mouth sensor, but rhythmic mouth opening did occur during specific phases of dives, suggesting that the turtle was relying on gustatory cues to sense its immediate environment. Patterns of diving in conjunction with these mouth-opening activities imply that leatherbacks are attempting to forage during the breeding season and that gustatory cues are important to leatherbacks.

Interactive exploration of data with visual metaphors

Visual Analytics (VA) is an approach to data analysis by means of visual manipulation of data representation, which relies on innate human abilities of perception and cognition. Even though current visual toolkits in the Business Analytics (BA) domain have improved the effectiveness of data exploration, analysis and reporting, their features are often not intuitive, and can be confusing and difficult to use. Moreover, visualizations generated from these toolkits are mostly accessible to specialist users. Thus, there is a need for analytic environments that support data exploration, interpretation and communication of insight that do not add to the cognitive load of the analyst and their non-technical clients. In this conceptual paper, we explore the potential of primary metaphors, which arise out of human lived and sensory-motor experiences, in the design of immersive visual analytics environments. Primary metaphors provide ideas for representation of time, space, quantity, similarity, actions and team work. Using examples developed in our own work, we also explain how to combine such metaphors to create complex and cognitively acceptable visual metaphors, such as 3D data terrains that approximate our intuition of reality and create opportunities for data to be viewed, navigated, explored, touched, changed, discussed, reported and described to others, individually or collaboratively.