Jaime B. Santos

Automatic defects classification — a contribution

Abstract The objective of this work is to provide a contribution to defect classification. More precisely, we try to prove that it is possible to identify and classify defects of different types using the pulse-echo technique. The classification process makes use of the time and frequency domain responses of the ultrasonic echo signals acquired from different specimens simulating defects with three different shapes (cylindrical, spherical and planar with rectangular cross-section) and sizes. Although the final goal is the characterisation of practical defects (for instance, voids, cracks, delaminations, and so on) appearing in composite materials during manufacturing and in service, we first use the already mentioned reflectors for simplicity reasons. In these experiments 66 reflectors are used with water as matrix material. The inclusion (reflector) materials are brass, copper, steel and polystyrene. From the time domain signals we extract three features, namely, pulse duration, pulse decay rate and peak-to-peak relative amplitude of the third cycle. From the spectra of the echoes we extract the frequency for maximum amplitude and the standard error estimate from the deconvolved spectrum responses. All experimental signals were obtained using only one normal incident ultrasonic transducer aligned to maximise the direct reflected signal. In spite of the fact that this kind of configuration does not provide complete information about the characteristics of the geometries being studied, all the extracted features proved to be important discriminating factors of the geometrical classes considered, as will be demonstrated by making use of a pattern recognition technique for classification.

Phase Symmetry Approach Applied to Children Heart Chambers Segmentation: A Comparative Study

Segmentation of echocardiographic images presents a great challenge because these images contain strong speckle noise and artifacts. Besides, most ultrasound segmentation methods are semiautomatic, requiring initial contour to be manually identified in the images. In this paper, we propose an algorithm based on the phase symmetry approach and level set evolution, in order to extract simultaneously all heart cavities in a fully automatic way. The level set evolution uses a new logarithmic-based stopping function, which demonstrates to perform well in the boundary extraction. We compared our method with other level set approaches, the watershed technique, and the manual segmentation made by two physicians. The experimental work was based on echocardiography images of children. Similarity metrics, namely Pratt function, pixel mean error, and similarity angle have been used for the performance evaluation of the different methods. The results indicate that our method has a performance of at least 4% superior to the other methods able to segment the four chambers. Even for the two worst boundary extraction cases (right ventricle and left atrium), the performance of the proposed method is still better than the other techniques.

Using ultrasound backscattering signals and Nakagami statistical distribution to assess regional cataract hardness.

This study aims to analyze the protein aggregates spatial distribution for different cataract degrees, and correlate this information with the lens acoustical parameters and by this way, assess the cataract regional hardness. Different cataract degrees were induced ex vivo in porcine lenses. A 25 MHz ultrasonic transducer was used to obtain the acoustical parameters (velocity, attenuation, and backscattering signals). B-scan and Nakagami images were constructed. Also, lenses with different cataract degrees were sliced in two regions (nucleus and cortex), for fibers and collagen detection. A significant increase with cataract formation was found for the velocity, attenuation, and brightness intensity of the B-scan images and Nakagami m parameter (p <; 0.01). The acoustical parameters showed a good to moderate correlation with the m parameter for the different stages of cataract formation. A strong correlation was found between the protein aggregates in the cortex and the m parameter. Lenses without cataract are characterized using a classification and regression tree, by a mean brightness intensity ≤0.351, a variance of the B-scan brightness intensity ≤0.070, a velocity ≤1625 m/s, and an attenuation ≤0.415 dB/mm·MHz (sensitivity: 100% and specificity: 72.6%). To characterize different cataract degrees, the m parameter should be considered. Initial stages of cataract are characterized by a mean brightness intensity >0.351 and a variance of the m parameter >0.110. Advanced stages of cataract are characterized by a mean brightness intensity >0.351, a variance of the m parameter ≤0.110, and a mean m parameter >0.374. For initial and advanced stages of cataract, a sensitivity of 78.4% and a specificity of 86.5% are obtained.

A level set segmentation method of the four heart cavities in pediatric ultrasound images

Echocardiography is the most used medical imaging in pediatric cardiology. It is a fundamental tool to analyze the major heart disease and abnormalities since it is non invasive and simple to use for physicians even when the children are wiggle. Ultrasound images are very noisy, making the segmentation a difficult, not accurate and time consuming task. In this work we propose an automatic segmentation method to extract the four heart cavity boundaries using a new pre-processing algorithm, based on phase symmetry. Experimental results using real echocardiographic images of children show good performance of the proposed method, providing a reliable tool to segment the heart walls that can be helpful for clinical practice.

Algorithm Versus Physicians Variability Evaluation in the Cardiac Chambers Extraction

Congenital heart diseases are present in eight of every 1000 newborns. The diagnosis of those pathologies usually depends on the available imaging methods. A correct diagnosis requires a detailed observation of the heart chambers, wall motions, valves function, and quantitative evaluation of the cavity volumes. For that goal numerous automatic algorithms have been proposed to segment the echocardiographic images. In this paper, the authors evaluate the performance of a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function to extract the heart cavity contours simultaneously, and in a fully automatic way. The extracted cardiac borders are then statistically compared with the ones manually sketched by four physicians on a set of 240 cavities. Nonparametric statistical tests are conducted on the data using several figures of merit, in order to study the inter- and intraobserver variabilities among the four physicians and the level set algorithm, concerning to the extracted contours. The results show there is a great concordance about all the used similarity indexes. A higher interobserver variability was found among the physicians than the variability obtained when the algorithm versus physician performance is compared. The statistical analysis suggests the proposed algorithm produces results similar to the ones provided by the physicians.

Imaging Particulate Two-Phase Flow in Liquid Suspensions with Electric Impedance Tomography

Different approaches have been followed to model the hydraulic transport of particles, ranging from pure empirical correlations to general models based on fundamental principles. However, these models suffer from uncertainties associated with the parameters in the constitutive equations and scarcity of experimental data in the literature. Nonintrusive techniques such as electric impedance tomography (EIT) can be used to circumvent the difficulties associated with sampling techniques. EIT is an imaging technique for the phase distribution in a two-phase flow field, allowing reconstructing the resistivity/conductivity distribution gradients from electrical data in a medium subjected to arbitrary excitations. Our best efforts were concentrated on the development of a new EIT system that is analogue based, portable, low-cost, and capable of providing high-quality sharp images when used to characterize the flow of particle suspensions. A voltage source was used, rather than a more complex and costly current source, since it provided the EIT system with a more precise and flexible current output. The data acquisition system consists of 16 electrodes equally spaced in the boundary of a tube and a custom dedicated electronic apparatus. The software supplies results in the form of two-dimensional reconstructed images that allow mapping the phase distribution inside the tube.

Lamb Waves Technique Applied to the Characterization of Defects in Friction Stir Welding of Aluminum Plates: Comparison with X-Ray and Ultrasonic C-Scan

Friction stir welding presents several advantages when compared with conventional arc welding processes, mainly in the welding of aluminum alloys. However, this welding technology leads to some degradation in the mechanical properties of welds, namely defect formation, which demands suitable nondestructive testing methods. The most common defects are mainly cold laps and voids, as a result of the large plastic deformation and hardening of the material as well as its complex flow behavior. In particular, the void appearance frequency may be correlated with the weld travel speed, though other welding parameters may contribute to the phenomenon. The purpose of this paper is to characterize such defects using conventional x-rays and ultrasonic C-scan, and a new method based on ultrasonic guided waves. The proposed new method presents as an attractive solution when large structures need to be inspected since propagation over long distances from a single probe position is possible with low attenuation. Additional characteristics such as straightforward inspection and testing fastness make the technique very cost effective. Test samples were fabricated using aluminum alloys of 3 mm in thickness, with different travel speeds and overlapping welds. This welding procedure gave rise to different defect sizes. Experimental results using both conventional and ultrasonic guided waves methods have confirmed the presence of the defects.

In-Vivo Automatic Nuclear Cataract Detection and Classification in an Animal Model by Ultrasounds

Objective: To early detect nuclear cataract in vivo and automatically classify its severity degree, based on the ultrasound technique, using machine learning. Methods: A 20-MHz ophthalmic ultrasound probe with a focal length of 8.9 mm and an active diameter of 3 mm was used. Twenty-seven features in time and frequency domain were extracted for cataract detection and classification with support vector machine (SVM), Bayes, multilayer perceptron, and random forest classifiers. Fifty rats were used: 14 as control and 36 as study group. An animal model for nuclear cataract was developed. Twelve rats with incipient, 13 with moderate, and 11 with severe cataract were obtained. The hardness of the nucleus and the cortex regions was objectively measured in 12 rats using the NanoTest. Results: Velocity, attenuation, and frequency downshift significantly increased with cataract formation (P <; 0.001). The SVM classifier showed the higher performance for the automatic classification of cataract severity, with a precision, sensitivity, and specificity of 99.7% (relative absolute error of 0.4%). A statistically significant difference was found for the hardness of the different cataract degrees (P = 0.016). The nucleus showed a higher hardness increase with cataract formation (P = 0.049). A moderate-to-good correlation between the features and the nucleus hardness was found in 23 out of the 27 features. Conclusion: The developed methodology made possible detecting the nuclear cataract in-vivo in early stages, classifying automatically its severity degree and estimating its hardness. Significance: Based on this work, a medical prototype will be developed for early cataract detection, classification, and hardness estimation.

New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers

In the present work, ultrasound A-scan signals were acquired from healthy and cataractous porcine lenses. B-mode images were reconstructed from the collected signals. The parametric Nakagami images were subsequently constructed from the B-mode images. Acoustical and spectral parameters were obtained from the central region of the lens. Image textural parameters were extracted from the B-scan and Nakagami images. Ninety-seven parameters were extracted from a total of 75 healthy and 135 cataractous lenses. Lenses with cataract were split in two groups: incipient and advanced cataract, corresponding to a 60 and 120 minutes of immersion time in a cataract induction solution, respectively. The obtained parameters were subjected to feature selection with Principal Component Analysis (PCA) and used for classification through a multiclass Support Vector Machine (SVM). This paper shows that multiclass SVM can perform effectively the classification of the cataract severity, with an overall performance of 89%, classifying correctly 93% of the features.

Automatic Defects Classification System Using Spectral Analysis and Neural Networks

This paper deals with the ultrasonic signal analysis using the frequency spectrum and neural networks. As our aim with this work is to obtain information about defect characteristics, namely shape, size and physical nature, we describe an ultrasonic spectroscopy test system in order to perform the acquisition, sampling, digitizing and the signal processing by the Fast Fourier Transform (FFT). The amplitude transfer function technique is used to extract information of the defects. The inclusions were fabricated with different shapes, sizes and of different materials. To make the automatic classification of defects a neural network using the Back-Propagation training algorithm was implemented.