The aim of this study was to investigate the value of fractal dimension in separating normal and cancerous images, and to examine the relationship between fractal dimension and traditional texture analysis features. Forty-four normal images and 58 cancer images from sections of the colon were analyzed. A "leave-one-out" analysis approach was used to classify the samples into each group. With fractal analysis there was a highly significant difference between groups (p < 0.0001). Correlation and entropy features showed greater differences between the groups (p < 0.0001). Nevertheless, the addition of fractal analysis to the feature analysis improved the sensitivity from 90% to 95% and specificity from 86% to 93%.

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 the statistical and artificial neural-network-based methods.

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An integrated Lifetime Health Record (LHR) is fundamental for achieving seamless and continuous access to patient medical information and for the continuum of care. However, the aim has not yet been fully realised. The efforts are actively progressing around the globe. Every stage of the development of the LHR initiatives had presented peculiar challenges. The best lessons in life are those of someone else's experiences. This paper presents an overview of the development approaches undertaken by four East Asian countries in implementing a national Electronic Health Record (EHR) in the public health system. The major challenges elicited from the review including integration efforts, process reengineering, funding, people, and law and regulation will be presented, compared, discussed and used as lessons learned for the further development of the Malaysian integrated LHR.

In recent years, a number of countries have introduced plans for national electronic patient record (EPR) systems. This paper argues that, in the near future, both patients and healthcare stakeholders will be able to access medical records from WWW-based EPR systems. We contend that the primary impediment to the successful implementation and widespread uptake of the EPR concept is the fact that current healthcare information security (HIS) applications are not sufficiently robust. This paper identifies two main Information Security technologies: 1) Public key infrastructure (PIU) and 2) Biometrics that hold a lot of promise in a healthcare context. The key contribution of this paper is to propose a novel multi-layered HIS framework based on a combination of PIU, Smartcard and Biometrics technologies. We argue that this new HIS framework could assist healthcare institutions to provide a truly secure infrastructure for the electronic transmission of clinical data in the future. This paper also makes a case for the creation of a new nodal HIS body because existing information security bodies like the Forum of Incident Response and Security Teams are for general-purpose organizations and not specifically suited for the healthcare sector.