João Manuel R. S. Tavares

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

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.

Efficient supervised optimum-path forest classification for large datasets

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.

Evaluation of Delamination Damage on Composite Plates using an Artificial Neural Network for the Radiographic Image Analysis

Drilling carbon/epoxy laminates is a common operation in manufacturing and assembly. However, it is necessary to adapt the drilling operations to the drilling tools correctly to avoid the high risk of delamination. Delamination can severely affect the mechanical properties of the parts produced. Production of high quality holes with minimal damage is a key challenge. In this article, delamination caused in laminate plates by drilling is evaluated from radiographic images. To accomplish this goal, a novel solution based on an artificial neural network is employed in the analysis of the radiographic images.

Biomechanical and neurophysiological mechanisms related to postural control and efficiency of movement: a review.

Understanding postural control requires considering various mechanisms underlying a persons ability to stand, to walk, and to interact with the environment safely and efficiently. The purpose of this paper is to summarize the functional relation between biomechanical and neurophysiological perspectives related to postural control in both standing and walking based on movement efficiency. Evidence related to the biomechanical and neurophysiological mechanisms is explored as well as the role of proprioceptive input on postural and movement control.

A New Solution for Automatic Microstructures Analysis from Images Based on a Backpropagation Artificial Neural Network

This article presents a new solution to segment and quantify the microstructures from images of nodular, grey, and malleable cast irons, based on an artificial neural network. The neural network topology used is the multilayer perception, and the algorithm chosen for its training was the backpropagation. This solution was applied to 60 samples of cast iron images and results were very similar to the ones obtained by visual human tests. This was better than the information obtained from a commercial system that is very popular in this area. In fact, this solution segmented the images of microstructures materials more efficiently. Thus, we can conclude that it is a valid and adequate option for researchers, engineers, specialists, and professionals from materials science field to realise a microstructure analysis from images faster and automatically.

Linear algebra algorithms in a heterogeneous cluster of personal computers

Cluster computing is presently a major research area, mostly for high performance computing. The work presented refers to the application of cluster computing in a small scale where a virtual machine is composed of a small number of off-the-self-personal computers connected by a low cost network. A methodology to determine the optimal number of processors to be used in a computation is presented as well as the speedup results obtained for the matrix-matrix multiplication and for the symmetric QR algorithm for eigenvector computation which are significant building blocks for applications in the target image processing and analysis domain. The load balancing strategy is also addressed.

Computational methods for the image segmentation of pigmented skin lesions

BACKGROUND AND OBJECTIVES Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation. METHODS Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle. RESULTS The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results. CONCLUSIONS The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.

Automatic 3D pulmonary nodule detection in CT images

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Novel Approach to Segment the Inner and Outer Boundaries of the Bladder Wall in T2-Weighted Magnetic Resonance Images

Diagnosis of bladder-related conditions needs critical measurements which require the segmentation of the inner and outer boundaries of the bladder wall. In T2-weighted MR images, the low-signal intensity bladder wall can be identified due to the large contrast with the high-signal intensity urine and perivesical fat. In this article, two deformable models are proposed to segment the bladder wall. Based on the imaging features of the bladder, a modified geodesic active contour is proposed to segment the inner boundary. This method uses the statistical information of the bladder lumen and can handle the intensity variation in MR images. Having obtained the inner boundary, a shape influence field is formed and integrated with the Chan–Vese (C–V) model to segment the outer boundary. The shape-guided C–V model can prevent the overlapping between the two boundaries when the appearance of the bladder wall is blurred. Segmentation examples are presented and analyzed to demonstrate the effectiveness of this novel approach.