Juan Gabriel Aviña-Cervantes

Active contours driven by Cuckoo Search strategy for brain tumour images segmentation

An alternative Active Contour Model solution for medical images is introduced.A multi-population Cuckoo Search Strategy (MCSS) is implemented to boost ACM.Proposed method was applied on Magnetic Resonance Imaging (MRI) data.MCSS outperforms traditional ACM and ACM driven by multi-population PSO. In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global minimum of the energy function, unlike the traditional ACM version which is often trapped in a local minimum. In the proposed methodology, each control point is constrained in a local search window, and its energy minimisation is performed through a Cuckoo Search via Levy flights paradigm. With respect to local search window, two shape approaches have been considered: rectangular shape and polar coordinates. Results showed that the CS method using polar coordinates is generally preferable to CS performed in rectangular shapes. Real medical and synthetic images were used to validate the proposed strategy, through three performance metrics as the Jaccard index, the Dice index and the Hausdorff distance. Applied specifically to Magnetic Resonance Imaging (MRI) images, the proposed method enables to reach better accuracy performance than the traditional ACM formulation, also known as Snakes and the use of Multi-population Particle Swarm Optimisation (PSO) algorithm.

Quantitative Estimation of Temperature Variations in Plantar Angiosomes: A Study Case for Diabetic Foot

Thermography is a useful tool since it provides information that may help in the diagnostic of several diseases in a noninvasive and fast way. Particularly, thermography has been applied in the study of the diabetic foot. However, most of these studies report only qualitative information making it difficult to measure significant parameters such as temperature variations. These variations are important in the analysis of the diabetic foot since they could bring knowledge, for instance, regarding ulceration risks. The early detection of ulceration risks is considered an important research topic in the medicine field, as its objective is to avoid major complications that might lead to a limb amputation. The absence of symptoms in the early phase of the ulceration is conceived as the main disadvantage to provide an opportune diagnostic in subjects with neuropathy. Since the relation between temperature and ulceration risks is well established in the literature, a methodology that obtains quantitative temperature differences in the plantar area of the diabetic foot to detect ulceration risks is proposed in this work. Such methodology is based on the angiosome concept and image processing.

Detection of Biological Cells in Phase-Contrast Microscopy Images

In this paper, we propose an automatic method to obtain cells detection and cells migration tracking in order to analyze cells behaviors under different conditions. The images were obtained using phase-contrast video microscopy method. Proposed method normalizes original images in order to increase image contrast, and a classification process based on variance operator determines the nature of pixels in the image as cells or background. Each detected cell is associated to its centroid in order to initialize the tracking procedure to quantify the migration process. This technique is a fast way to describe cells migrations, robust to cell contracts and mitosis, all over their trajectories.

Total internal reflection of spatial solitons at interface formed by a nonlinear saturable and a linear medium

Abstract We study numerically and experimentally the reflection of spatial solitons at the interface between a nonlinear saturable-type medium and a linear one. We emphasize on determining the physical conditions under which the reflected beam at the interface conserve its nondiffracting properties. Depending on the incidence angle, we find three critical regions for spatial soliton conservation after reflection. We numerically show that the nonlinear Goos–Hanchen shift can have a dramatic effect on the diffracting properties of the reflected beam.

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

A CPG system based on spiking neurons for hexapod robot locomotion

In this paper, we propose a locomotion system based on a central pattern generator (CPG) for a hexapod robot, suitable for embedded hardware implementation. The CPG system was built as a network of spiking neurons, which produce rhythmic signals for three different gaits (walk, jogging and run) in the hexapod robot. The spiking neuron model used in this work is a simplified form of the well-known generalized Integrate-and-Fire neuron model, which can be trained using the Simplex method. The use of spiking neurons makes the system highly suitable for digital hardware implementations that exploit the inherent parallelism to replicate the intrinsic, computationally efficient, distributed control mechanism of CPGs. The system has been implemented on a Spartan 6 FPGA board and fully validated on a hexapod robot. Experimental results show the effectiveness of the proposed approach, based on existing models and techniques, for hexapod rhythmic locomotion.

Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Eigen-Gradients for Traffic Sign Recognition

Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

Access Control System Using an Embedded System and Radio Frequency Identification Technology

The radio frequency identification technique has been known since decades ago, however due to some important advances in technology, the amount of micro devices built-in on a chip and the cost of manufacturing, this technology has been implemented in many applications nowadays. A radio frequency identification system used to security check in a University is presented in this work. In this system several autonomous embedded sub-systems are included, which are placed at different entrances at school. Every embedded sub-system has a link up to a master control that registers every event at the entrance. This master control has the command to close or open any entry at any time if a case of emergency is occurred.A detailed analysis about the elements that are included in this autonomous access control is presented. Some important aspects of this system are also presented. Conclusions and perspectives are presented at the end of this work.

Monte carlo evaluation of the hausdorff distance for shape matching

In this work, we present a Monte Carlo approach to compute Hausdorff distance for locating objects in real images. Objects are considered to be only under translation motion. We use edge points as the features of the model. Using a different interpretation of the Hausdorff distance, we show how image similarity can be measured by using a randomly sub-sampled set of feature points. As a result of computing the Hausdorff distance on smaller sets of features, our approach is faster than the classical one. We have found that our method converges toward the actual Hausdorff distance by using less than 20 % of the feature points. We show the behavior of our method for several fractions of feature points used to compute Hausdorff distance. These tests let us conclude that performance is only critically degraded when the sub-sampled set has a cardinality under 15 % of the total feature points in real images.