Michael O. Odetayo

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.

Enhancing the low quality images using Unsupervised Colour Correction Method

Underwater images are affected by reduced contrast and non-uniform colour cast due to the absorption and scattering of light in the aquatic environment. This affects the quality and reliability of image processing and therefore colour correction is a necessary pre-processing stage. In this paper, we propose an Unsupervised Colour Correction Method (UCM) for underwater image enhancement. UCM is based on colour balancing, contrast correction of RGB colour model and contrast correction of HSI colour model. Firstly, the colour cast is reduced by equalizing the colour values. Secondly, an enhancement to a contrast correction method is applied to increase the Red colour by stretching red histogram towards the maximum (i.e., right side), similarly the Blue colour is reduced by stretching the blue histogram towards the minimum (i.e., left side). Thirdly, the Saturation and Intensity components of the HSI colour model have been applied for contrast correction to increase the true colour using Saturation and to address the illumination problem through Intensity. We compare our results with three well known methods, namely Gray World, White Patch and Histogram Equalisation using Adobe Photoshop. The proposed method has produced better results than the existing methods.

Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics

In this paper, we discuss a new content-based image retrieval approach for biometric security, which is based on colour, texture and shape features and controlled by fuzzy heuristics. The proposed approach is based on the three well-known algorithms: colour histogram, texture and moment invariants. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and similarity metrics based on Euclidean measure. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The evaluation of the proposed approach is carried out using the standard precision and recall measures, and the results are compared with the well-known existing approaches. We present results which show that our proposed approach performs better than these approaches.

Empirical study of the interdependencies of genetic algorithm parameters

Although it is recognised that the performance of evolutionary systems such as genetic algorithms (GAs) is affected by the parameters that are employed to implement them, there is hardly any work known to us that has shed much light on the interdependencies and interactions between these parameters. Most studies on the effects of these parameters on the performance of GA-based systems have focused on a parameter at a time without considering the effect of other parameters on that parameter and vice versa. Consequently there is hardly any theory about the interactions and interdependencies of these parameters. This paper contributes towards correcting the situation mentioned above by examining empirically the relationship between two parameters of genetic algorithms (GAs): population size and replacement methods in the performance of GA-based systems. Results are presented that appear to show a link between replacement strategy and an appropriate population size when applying genetic algorithms to a particular problem. It is suggested that, in the domain of application considered in this paper one can infer that the more individuals that are replaced during reproduction the larger the population size that is needed for all optimum performance of GA-based systems. It is suggested that directing our efforts towards establishing the interdependencies and interactions between parameters of evolutionary systems will enhance the advancement of this new technology.

A fuzzy measurement-based assessment of breast cancer prognostic markers

The paper aims to assess breast cancer prognostic markers and to determine an optimum subset that can yield high prediction accuracy for an individual breast cancer patients prognosis by means of a fuzzy measurement derived from the fuzzy k-nearest neighbour algorithm (FK-NN). The analyses are carried out for both nodal involvement and five-year survival. The data set used for the analysis of breast cancer prognosis consists of seven input markers (histology type, grade, DNA ploidy, S-Phase Fraction (SPF), G/sub 0/G/sub 1//G/sub 2/M ratio, minimum and maximum nuclear pleomorphism indices (NPI)) and two corresponding outputs to be predicted (negative or positive nodal status in the case of nodal involvement assessment, and whether the patient is alive or dead within 5 years of diagnosis for survival analysis). The highest predictive accuracy is 78% with the fuzzy measurement of 0.7254 for nodal involvement assessment, and 88% with the fuzzy measurement of 0.8183 for survival analysis. The best results are obtained from the subset (Histology type, Grade, DNA. Ploidy, SPF (%), G/sub 0/G/sub 1//G/sub 2/M Ratio) for survival prediction and the subset (Grade, SPF, minimum NPI) for nodal involvement analysis.

Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches

Two different approaches, based on artificial neural networks (ANN) and fuzzy logic, were used to predict a number of outcomes of newborns: How they would be delivered, their 5 minute Apgar score, and neonatal mortality. The goal was to assess whether the methods would be comparable or whether they would perform differently for different outcomes. The results were comparable for Correct Classification Rate (CCR) and Specificity (true negative cases). Sensitivity (true positive cases) was slightly higher for the back-propagation feed-forward ANN than using the Fuzzy-Logic Classifier (FLC). Since this is one single database and a very large one, it is possible that the FLC would perform better than the ANN for very small databases, as shown by some of the co-authors in the past. The next step will be to test a small database with both methods to assess strengths and weaknesses with the intent to use both if needed with some medical data in the future.

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The results show the proposed approaches perform better than the well-known existing approaches.

Mediation Architecture for Integration of Heterogeneous Discipline Focused Workflow Languages

Workflow plays an important role in Service-Oriented Architecture, as service composition requires workflow to link services together to meet requirements from users. A number of workflows with different characteristics such as representation and logic to satisfy different demands from various problem domains (e.g. engineering, business, and scientific works) have been developed and used. However, many of these languages and their supporting enactment engines are not compatible with each other due to a lack of coherent development standards and interface protocols. An enterprise often involves multiple disciplinary software systems which need different workflow languages to model their activities, so the integration of these languages to support the integrity of enterprise tasks and activities becomes inevitable. This paper describes a new approach which allows different existing workflow languages with their supporting engines to work seamlessly together by introducing a mediation architecture which can maintain the transition states and data consistency among different workflow languages and also coordinate their execution engines. So, a workflow can be represented in different languages and carried out in a distributed environment. An example will be used to illustrate the key functions in the proposed mediating service and to demonstrate the use in a business environment.

Metadata Discovery for a Service-Broker Architecture

UDDI (universal description, discovery and integration) is one of the main standards for describing, publishing and discovering services on the Internet. It employs a service-broker mechanism through service storage and registration. We will discuss the need for data enquiry services that are best published with their underlying metadata. In order to provide greater utility and expressivity it is proposed to represent the metadata in a Web ontology. This needs to be achieved within the UDDI so that such data enquiry services are easily accessible. We propose an extended template for publishing, registering and storing services that facilitates the discovery of metadata in a service broker architecture such as UDDI. Finally we illustrate the operation of this extended use of UDDI using existing approaches and standard technologies.

Face detection of ubiquitous surveillance images for biometric security from an image enhancement perspective

Security methods based on biometrics have been gaining importance increasingly in the last few years due to recent advances in biometrics technology and its reliability and efficiency in real world applications. Also, several major security disasters that occurred in the last decade have given a new momentum to this research area. The successful development of biometric security applications cannot only minimise such threats but may also help in preventing them from happening on a global scale. Biometric security methods take into account humans’ unique physical or behavioural traits that help to identify them based on their intrinsic characteristics. However, there are a number of issues related to biometric security, in particular with regard to the poor visibility of the images produced by surveillance cameras that need to be addressed. In this paper, we address this issue by proposing an integrated image enhancement approach for face detection. The proposed approach is based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It can adjust the colour cast and maintain the luminance of the whole image at the same level. We evaluate the performance of the proposed approach by applying three face detection methods (skin colour based face detection, feature based face detection and image based face detection) to surveillance images before and after enhancement using the proposed approach. The results show a significant improvement in face detection when the proposed approach was applied.