Breast microcalcifications are one of the most important mammographic findings related to the existence of the breast cancer. Radiologists usually characterize microcalcifications based on their morphologies, the distribution within the cluster they form, the shape of the cluster and its relative location inside the breast. In this study, we focus on the latter factor and we study its effect on the probability of malignancy. The main purpose of our study is to generate probabilistic breast cancer atlases for clusters of microcalcifications in order to visualize the influence of cluster location on cancer probability. We propose a framework for the generation of such atlases, including segmentation of important breast landmarks and projection of different clusters of microcalcifications on a reference breast shape. The generation of the atlases is implemented using mammograms from the Digital Database of Screening Mammography. The obtained probabilistic atlases reveal specific areas in the breast of higher occurrence of clusters and higher risk of malignancy.

In this work, we study the factors that influence the efficacy of a proposed Computer Aided Diagnosis (CADx) framework for the diagnosis of clustered microcalcifications (MCs) using a large dataset of mammograms containing cases of varying breast density and findings' subtlety. The reported results indicate that the proposed framework performs towards the right direction, as it appears high classification performance (Az=0.909) for specific subsets of cases, while outperforming at the same time the performance of the radiologists who evaluated the same cases. The effect of the initial enhancement of mammograms in the CADxpipeline is then investigated, by applying three different image enhancement techniques on several subsets of mammograms. We observed that for the considered subsets of dense mammograms, a wavelet-based enhancement algorithm outperformed the rest and provided superior classification performance (Az=0.849). We indicate therefore that the density of the breast determines the need of different computational algorithms for the analysis of a mammogram and as a result the a priori knowledge of this factor may be exploited for the optimization of the diagnostic process.

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of Computational Intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.

Breast cancer diagnosis requires specific expertise from the Medical Doctors. Furthermore, prognosis, monitoring and early detection of malignant findings can be successfully realized through synergies between physicians, researchers and general population in concert with the health policy program of each country. Information technology and computational intelligence play a crucial role in the production of digital repositories and cancer registries as well as in the development of systems to support diagnosis. In this paper we present the concept and the architecture of an approved grant under the National Strategic Reference Framework 2007-2013 (NSRF), called e-Prolipsis. Through this project, we will design and implement a web based risk estimation platform and a Central Breast Cancer Registry (CBCR) that will co-operate to provide medical doctors and patients with services such as multiplicity in diagnostic opinions, synergy, computational risk estimation, access to statistical/epidemiological data for trend estimation. Such a system, will serve as a reference tool and will help the clinician, the radiologist, the rural doctor or trainee doctor in the final assessment on the existence or likelihood of breast cancer. The system will assist in the successful diagnosis of breast cancer, giving each patient access to a large pool of doctors and at the same time, the data stored in the CBCR will be used for statistical analysis, providing useful results for improving both the cancer detection application and for making a national policy for combating breast cancer. This system will be accessible through a Web Portal, with different access levels for patients, doctors and general public and different web services available to each user group, eliminating the geographical distances that would be imposed otherwise.

A hybrid neural-digital computer-aided diagnosis (N.CADx) system is proposed for early detection and classification of cancerous lung nodules of size 3-15 mm. Digital image processing techniques are used for noise reduction, image enhancement, and "suspect" search and localization. The neural classification concerns feature extraction and diagnosis of a particular pattern-class aimed at high degree of "true positive fraction" detection and low "false positive fraction". Applying fuzzy linguistic concepts, we have developed a multi-label output encoding procedure, for purposes of neural training and for interpretation of the activity distributions in the output neurons. Over five output neurons, that were used in the nodule diagnosis problem, the activity distribution is interpreted by the centroid as the normalized disease index-NDI, or as a nodule detection probability-NDP value associated with a confidence level factor. The proposed N.CADx system has great potential in many medical applications.>

The author examines the real-time capture of experiential knowledge, based on formal modeling of human perception by presenting algorithms for the recognition and labeling of behavioral patterns. The nature of human perceptual models is discussed and the features of the FDS (formal description schema) model of human perception are presented. Real-time algorithms for recognition and labeling of behavioral patterns are then discussed. It is concluded that only by the approximation of modeling is it possible to gain efficacy in experiential knowledge representation and in 'knowing'.>

Two complement representations and a sign-magnitude one are introduced which allow for handling negative numbers using only binary coefficients in Fibonacci base expansions. These are developed for practical implementation in Fibonacci computers.

A fast-division algorithm, effectively implementable with available IC technology, has been developed and implemented. Especially suited for asynchronous division units, the skip-and-set algorithm provides simple control provisions for the division of numbers in any format and to any fractional precision.