A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

Medical imaging mainly manages and processes missing, ambiguous, omplementary, redundant and distorted data and information has a strong structural character. This paper reports a new (semi)automated and supervised method for the segmentation of brain structures using a rule-based fuzzy system. In the field of biomedical image analysis fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information, as well as structural information constituted mainly by spatial relationships. The developed application is for the segmentation of brain structures in CT (computer tomography) images. Promising results show the superiority of this knowledge-based approach over best traditional techniques in terms of segmentation errors. The quantitative assessment of this method is made by comparing manually and automatic segmented brain structures by using some indexes evaluating the accuracy of contour detection and spatial location. Though the proposed methodology has been implemented and successfully used for modeldriven in medical imaging, it is general enough and may be applied to any imagistic object that can be expressed by expert knowledge and morphological images

Biomedical Image Registration by means of Bacterial Foraging Paradigm

Image registration (IR) is the process of geometric overlaying or alignment f two or more 2D/3D images of the same scene (unimodal registration), taken r not at different time slots, from different angles, and/or by different image acquisition ystems (multimodal registration). Technically, image registration implies  complex optimization of different parameters, performed at local or/and global evel. Local optimization methods often fail because functions of the involved metrics ith respect to transformation parameters are generally nonconvex and irregular, and lobal methods are required, at least at the beginning of the procedure. This paper resents a new evolutionary and bio-inspired robust approach for IR, Bacterial Foraging ptimization Algorithm (BFOA), which is adapted for PET-CT multimodal nd magnetic resonance image rigid registration. Results of optimizing the normalized utual information and normalized cross correlation similarity metrics validated he efficacy and precision of the proposed method by using a freely available medical mage database

Parkinson’s Disease Prediction Based on Multistate Markov Models

In the real medical world, there are many symptoms or chronic diseases that cannot be characterized in a deterministic way, and which must be examined in a random way. In the study of these stochastic processes, Markov chains are used. There is a wide variety of phenomena that suggest a behavior in a Markov process manner such as: the probability that a patient's health to improve, to get worse, to remain stable or to progress to death within a certain time slot, depending on what happened in the previous time window. Our goal is to show that the Markov chains can be applied to the patients with Parkinson’s disease in order to predict the evolution of the disease over time. So the doctor may decide a therapeutic solution that is adapted to the patient's needs, and that can improve the quality of the patient's life with Parkinson's disease in terminal stage

Medical Image Registration by means of a Bio-Inspired Optimization Strategy

Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant and distorted data. Biomedical image registration is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene, taken at different time slots, from different angles, and/or by different acquisition systems. In medical practice, it is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Technically, image registration implies a complex optimization of different parameters, performed at local or/and global levels. Local optimization methods frequently fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular. Therefore, global methods are often required, at least at the beginning of the procedure. In this paper, a new evolutionary and bio-inspired approach -- bacterial foraging optimization -- is adapted for single-slice to 3-D PET and CT multimodal image registration. Preliminary results of optimizing the normalized mutual information similarity metric validated the efficacy of the proposed method by using a freely available medical image database

E-Health System for Medical Telesurveillance of Chronic Patients

The current common goal in medical information technology today is the design and implementation of telemedicine solutions, which provide to patients services that enhance their quality of life. Advances in wireless sensor network technology, the overall miniaturization of their associated hardware low-power integrated circuits and wireless communications have enabled the design of low-cost, miniature, and intelligent physiological sensor modules with applications in the medical industry. These modules are capable of measuring, processing, communicating one or more physiological parameters, and can be integrated into a wireless personal area network. This paper is dedicated to the most complex Romanian telemedical pilot project, TELEMON, which has as goals design and implementation of an electronic-informaticstelecommunications system, that allows the automatic and complex telemonitoring, everywhere and every time, in (almost) real time, of the vital signs of persons with chronic illnesses, of elderly people, of those having high medical risk and of those living in isolated regions. The final objective of this pilot project is to enable personalized medical teleservices delivery, and to act as a basis for a public service for telemedical procedures in Romania and abroad

Intelligent Biosignal Processing in Wearable and Implantable Sensors

Wearable technology including sensors, sensor networks, and the associated devices have opened up space in a variety of applications [...