Visual Sensor Networks (VSNs) open up a new realm of smart autonomous applications based on enhanced three- dimensional sensing and collaborative reasoning. An emerging VSN application domain is pervasive healthcare delivery where gait information computed from distributed vision nodes is used for observing the wellbeing of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. The development of patient-specific gait analysis models, however, is challenging since it is unfeasible to obtain normal and impaired

Magnetic resonance imaging allows a number of imaging techniques and protocols that can be used to capture the different aspects of the cardiac function and structure. The produced amount of data is huge and its classification and/or retrieval based on its visual content are necessary for educational and training purposes. In this work, we propose a method for classification and retrieving cardiac magnetic resonance images based on the type of the acquisition technique. Preliminary results are obtained from two data sets of 3175 images acquired using five different cardiac imaging techniques

Human motion analysis is a vital research area for healthcare systems. The increasing need for automated activity analysis inspired the design of low cost wireless sensors that can capture information under free living conditions. Body and Visual Sensor Networks can easily record human behavior within a home environment. In this paper we propose a multiple classifier system that uses time series data for human motion analysis. The proposed approach adaptively integrates feature extraction and distance based techniques for classifying impaired and normal walking gaits. Information from body

Imaging of the heart anatomy and function using Strain Encoded (SENC) magnetic resonance imaging (MRI) is a powerful tool for diagnosing a number of heart diseases. Despite excellent sensitivity to tissue deformation, the technique inherently suffers from elevated noise level which hinders proper automatic segmentation using conventional techniques. In this work, we propose a method to accurately segment the left ventricle myocardium from strain encoded-MR short axis images. The method is based on a modified formulation of the graph cuts algorithm. A novel cost function based on a

Based registration of retinal images proved to be very successful especially for minimally overlapping images. The most commonly used transformation method uses a quadratic model to represent the geometry of the retinal surface. Although this model has been used for more than one decade, there is no literature that studies the model errors for abnormal eye geometries. In this work, we present a study of the registration errors of the quadratic model in case of diseased eyes. The study includes two basic models of the retinal surface for eyes suffering from: myopia; and retinal diseases (e.g

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac-induced deformation

Fundus Fluorescein Angiography (FA) is a powerful tool for imaging and evaluating Diabetic Macular Edema (DME), where the fluorescein dye leaks and accumulates in the diseased areas. Currently, the assessment of FA images is qualitative and suffers from large inter-observer variability. A necessary step towards quantitative assessment of DME is automatic segmentation of fluorescein leakage. In this work, we present an automatic method for segmenting DME areas in FA images. The method is based on modeling the macular image in the early time frame using 2D Gaussian surfaces, which is then

Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets - one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144

Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95%. We extended the approach to detecting the death of a given MN type using clustering validity index