A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Dynamic simulation of human motion

In order to study the mechanisms behind the human motion and its disorders, and to comprehend how do neuromuscular impairments affect the human movements, it is crucial to know the human musculoskeletal system. This knowledge is also essential to effectively model and analyse the musculoskeletal system, and to classify the human movement. With this work we conducted preliminary studies over a musculoskeletal model in motion, and performed a comparative analysis in gait between two models: one with a non-pathological gait and another with a disordered gait

An improved management model for tracking missing features in computer vision long image sequences

In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented

Contour detection by surround suppression of texture

Based 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

Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Today data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without losing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the training samples. In this forest, each class may be represented by multiple trees rooted at some representative samples. The forest is a classifier that assigns to a new sample the label of its most strongly connected root. The methodology has been successfully used with different graph topologies and learning techniques. In this work, we have focused on one of the supervised approaches, which has offered considerable advantages over Support Vector Machines and Artificial Neural Networks to handle large datasets. We propose (i) a new algorithm that speeds up classification and (ii) a solution to reduce the training set size with negligible effects on the accuracy of classification, therefore further increasing its efficiency. Experimental results show the improvements with respect to our previous approach and advantages over other existing methods, which make the new method a valuable contribution for large dataset analysis. (C) 2011 Elsevier Ltd. All rights reserved.451512520Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP) [35.0053/2011.1]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2009/16206-1, 2007/52015-0]CNPq [481556/2009-5, 303673/2010-91]Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP) [35.0053/2011.1

Hybrid algorithm for the classification of fractal designs and images

The fractal patterns are recursive patterns and are self similar in nature. The fractal geometry provides better understanding of natural patterns as compared to the euclidean geometry. The fractal designs have been used extensively in the fields of applied sciences due to the systematic methods used for their generation. These methods provide benchmarks to analyze the roughness, narrow/ broad vision of the objects. In the fields of architecture and design, computational methods for fractal generation can prove to be more reliable tools. The fractal patterns can be simulated and the architecture can be modeled, with several options, before implementing it practically. During this research, this strategy is opted to design novel fractal tile designs. Several designs are selected from a series of simulations, based on the final visionaryevaluation, according to the requirement of the walls of different zones in modern buildings. Four fractal patterns are simulated with several orientations and final designs are documented with corresponding geometrical evaluation

LVQ acrosome integrity assessment of boar sperm cells

We 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

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. (C) 2012 Elsevier Ltd. All rights reserved.402590597Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP), in Brazil through a DCR Grant [35.0053/2011.1]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CNPq [303673/2010-9, 303182/2011-3]FAPESP [2009/16206-1, 2011/14058-5]Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP), in Brazil through a DCR Grant [35.0053/2011.1