1,720,999 research outputs found
Predicting the Efficacy of Stalk Cells Following Leading Cells Through a Micro-Channel Using Morphoelasticity and a Cell Shape Evolution Model
No full textCancer cell migration between different body parts is the driving force behind cancer metastasis, which causes mortality of patients. Migration of cancer cells often proceeds by penetration through narrow cavities in possibly stiff tissues. In our previous work [12], a model for the evolution of cell geometry is developed, and in the current study we use this model to investigate whether followers among (cancer) cells benefit from leading (cancer) cells during transmigration through microchannels and cavities. Using Wilcoxon's signed-rank text on the data collected from Monte Carlo simulations, we conclude that the transmigration time for the stalk cell is significantly smaller than for the leading cell with a p-value less than 0.0001, for the modelling set-up that we have used in this study
A Spatial Markov Chain Cellular Automata Model for the Spread of Viruses
No full textIn this paper a Spatial Markov Chain Cellular Automata model for the spread of viruses is proposed. The model is based on a graph with connected nodes, where the nodes represent individuals and the connections between the nodes denote the relations between humans. In this way, a graph is connected where the probability of infectious spread from person to person is determined by the intensity of interpersonal contact. Infectious transfer is determined by chance. The model is extended to incorporate various lockdown scenarios. Simulations with different lockdowns are provided. In addition, under logistic regression, the probability of death as a function of age and gender is estimated, as well as the duration of the disease given that an individual dies from it. The estimations have been done based on actual data of RIVM (from the Netherlands)
Knowledge Sharing in Biomedical Imaging using a Grid Computing approach
No full textThis paper will present the Knowledge Grid based system model, the architecture and the de-sign principles focusing the discussion on the biomedical imaging process
Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ
Full text linkWe consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-damaged (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for each head. As a feature vector we use the gradient magnitude along the contour of the sperm head. We apply learning vector quantization (LVQ) to the feature vectors obtained for 320 heads that were labelled as intact or damaged using stains. A LVQ system with four prototypes (two for each class) allows us to classify cells with an overall test error of 6.8%. This is considered to be sufficient for semen quality control in an artificial insemination center.
Time-of-Flight Camera Based Virtual Reality Interaction for Balance Rehabilitation Purposes
No full textThe3DHumanBodyModels(3DHBMs)andthe3DVirtual Reality Environments (3D VREs) enable users to interact with simulated scenarios in an engaging and natural way. The Computer Vision (CV) based Motion Capture (MoCap) systems allow us to obtain user models (i.e., self-avatars) without using cumbersome and uncomfortable physical tools (e.g., sensor suites) which could adversely affect user experience. This last point is of great importance in developing interactive applica- tions for balance rehabilitation purposes where the recovery of lost skills is related to different factors (e.g., patient motivation) including spon- taneity of the interaction during the virtual rehabilitative exercises. This paper presents an overview of the Customized Rehabilitation Framework (CRF), a single range imaging sensor based system oriented to patients who experienced with brain strokes, head traumas or neurodegenerative disorders. In particular, the paper is focused on the implementation of two new ad-hoc virtual exercises (i.e., Surfboard and Swing) support- ing patients in recovering physical and functional balance. Observations on accuracy of user body models and their real-time interaction ability within rehabilitative simulated environments are presented. In addition, basic experiments concerning usefulness of the proposed exercises to sup- port balance rehabilitation purposes are also reported
Contour detection by surround suppression of texture
No full textBased on a keynote lecture at Complmage 2006, Coimbra, Oct. 20-21, 2006, an overview is given of our activities in modelling and using surround inhibition for contour detection. The effect of suppression of a line or edge stimulus by similar surrounding stimuli is known from visual perception studies. It can be related to non-classical receptive field (non-CRF) inhibition that is found in 80% of the orientation selective neurons in the primary visual cortex. A computational model of surround suppression is presented. It acts as a feature contrast computation for oriented stimuli: the response to an edge at a given position is suppressed by other edge responses in the surround. Consequently, the responses to texture edges are strongly reduced while the responses to contours are scarcely affected. The model gives results that are in line with perception. A surround suppression step is added to a Gabor energy filter and to the Canny edge detector. In either case it improves considerably the detection of contours. The biological utility of the neural mechanism of surround inhibition might be that of quick pre-attentive detection of object contours in natural environments rich in texture. In computer vision, a surround suppression step can be added to virtually any edge detector with limited local support in order to improve its contour detection performance.</p
LVQ acrosome integrity assessment of boar sperm cells
Full text linkWe consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-reacted (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for each head. As a feature vector we use the gradient magnitude along the contour of the sperm head. We apply learning vector quantization (LVQ) to the feature vectors obtained for 152 heads that were visually inspected and classified by a veterinary expert. A simple LVQ system with only three prototypes (two for class I and one for class 2) allows us to classify cells with equal training and test errors of 0.165. This is considered to be sufficient for semen quality control in an artificial insemination center.</p
LVQ acrosome integrity assessment of boar sperm cells
Full text linkWe consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-reacted (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for each head. As a feature vector we use the gradient magnitude along the contour of the sperm head. We apply learning vector quantization (LVQ) to the feature vectors obtained for 152 heads that were visually inspected and classified by a veterinary expert. A simple LVQ system with only three prototypes (two for class I and one for class 2) allows us to classify cells with equal training and test errors of 0.165. This is considered to be sufficient for semen quality control in an artificial insemination center.</p
Contour detection by surround suppression of texture
No full textBased on a keynote lecture at Complmage 2006, Coimbra, Oct. 20-21, 2006, an overview is given of our activities in modelling and using surround inhibition for contour detection. The effect of suppression of a line or edge stimulus by similar surrounding stimuli is known from visual perception studies. It can be related to non-classical receptive field (non-CRF) inhibition that is found in 80% of the orientation selective neurons in the primary visual cortex. A computational model of surround suppression is presented. It acts as a feature contrast computation for oriented stimuli: the response to an edge at a given position is suppressed by other edge responses in the surround. Consequently, the responses to texture edges are strongly reduced while the responses to contours are scarcely affected. The model gives results that are in line with perception. A surround suppression step is added to a Gabor energy filter and to the Canny edge detector. In either case it improves considerably the detection of contours. The biological utility of the neural mechanism of surround inhibition might be that of quick pre-attentive detection of object contours in natural environments rich in texture. In computer vision, a surround suppression step can be added to virtually any edge detector with limited local support in order to improve its contour detection performance.</p
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