Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Supercritical CO2 extraction of secondary metabolites from Agaricus blazei: experiments and modelling

The mycelium and young fruiting bodies of Agaricus blazei were submitted to supercritical CO2 extraction, in a modified commercial flow apparatus, at temperatures from 40 to 80 ºC, pressures up to 600 bar and CO2 flow-rates from 2.0 to 9.0 g.min-1. The best extraction conditions of secondary metabolites, whereby the degree of solubilization (g extract/100 g of fungi) is the highest, was obtained with pure CO2 at 400 bar, 70 ºC and a CO2 flow rate of 5.7g.min-1. The extract in that conditions were analysed by GC-Ms. In order to increase the extraction yield of secondary metabolites, which are mostly present in glycolipid fractions, a polar compound (ethanol) was used as co-solvent in the proportions of 5 and 10 % (mol/mol). The presence of ethanol increased the yield when compared with the extraction with pure CO2. Moreover, a simple model was applied to the supercritical CO2 extraction of secondary metabolites from Agaricus blazei.

Herbicidal activity of volatiles from coriander, winter savory, cotton lavender, and thyme isolated by hydrodistillation and supercritical fluid extraction

The volatiles from Coriandrum sativum L., Satureja montana L., Santolina chamaecyparissus L., and Thymus vulgaris L. were isolated by hydrodistillation (essential oil) and supercritical fluid extraction (volatile oil). Their effect on seed germination and root and shoot growth of the surviving seedlings of four crops (Zea mays L., Triticum durum L., Pisum sativum L., and Lactuca sativa L.) and two weeds (Portulaca oleracea L. and Vicia sativa L.) was investigated and compared with those of two synthetic herbicides, Agrocide and Prowl. The volatile oils of thyme and cotton lavender seemed to be promising alternatives to the synthetic herbicides because they were the least injurious to the crop species. The essential oil of winter savory, on the other hand, affected both crop and weeds and can be appropriate for uncultivated fields.

Composition and antioxidant activity of thymus vulgaris volatiles: comparison between supercritical fluid extraction and hydrodistillation

Supercritical fluid extraction (SEE) of the volatile oil from Thymus vulgaris L. aerial flowering parts was performed under different conditions of pressure, temperature, mean particle size and CO2 flow rate and the correspondent yield and composition were compared with those of the essential oil isolated by hydrodistillation (HD). Both the oils were analyzed by GC and GC-MS and 52 components were identified. The main volatile components obtained were p-cymene (10.0-42.6% for SFE and 28.9-34.8% for HD), gamma-terpinene (0.8-6.9% for SFE and 5.1-7.0% for HD), linalool (2.3-5.3% for SFE and 2.8-3.1% for HD), thymol (19.5-40.8% for SFE and 35.4-41.6% for HD), and carvacrol (1.4-3.1% for SFE and 2.6-3.1% for HD). The main difference was found to be the relative percentage of thymoquinone (not found in the essential oil) and carvacryl methyl ether (1.0-1.2% for HD versus t-0.4 for SFE) which can explain the higher antioxidant activity, assessed by Rancimat test, of the SFE volatiles when compared with HD. Thymoquinone is considered a strong antioxidant compound.

Mathematical modelling of supercritical CO2 extraction of volatile oils from aromatic plants

The modelling of the experimental data of the extraction of the volatile oil from six aromatic plants (coriander, fennel, savoury, winter savoury, cotton lavender and thyme) was performed using five mathematical models, based on differential mass balances. In all cases the extraction was internal diffusion controlled and the internal mass transfer coefficienty (k(s)) have been found to change with pressure, temperature and particle size. For fennel, savoury and cotton lavender, the external mass transfer and the equilibrium phase also influenced the second extraction period, since k(s) changed with the tested flow rates. In general, the axial dispersion coefficient could be neglected for the conditions studied, since Peclet numbers were high. On the other hand, the solute-matrix interaction had to be considered in order to ensure a satisfactory description of the experimental data.

Extraction of hemoglobin with calixarenes and biocatalysis in organic media of the complex with pseudoactivity of peroxidase

The present work involves the use of p-tert-butylcalix[4,6,8]arene carboxylic acid derivatives ((t)Butyl[4,6,8]CH2COOH) for selective extraction of hemoglobin. All three calixarenes extracted hemoglobin into the organic phase, exhibiting extraction parameters higher than 0.90. Evaluation of the solvent accessible positively charged amino acid side chains of hemoglobin (PDB entry 1XZ2) revealed that there are 8 arginine, 44 lysine and 30 histidine residues on the protein surface which may be involved in the interactions with the calixarene molecules. The hemoglobin-(t)Butyl[6]CH2COOH complex had pseudoperoxidase activity which catalysed the oxidation of syringaldazine in the presence of hydrogen peroxide in organic medium containing chloroform. The effect of pH, protein and substrate concentrations on biocatalysis was investigated using the hemoglobin-(t)Butyl[6]CH2COOH complex. This complex exhibited the highest specific activity of 9.92 x 10(-2) U mg protein(-1) at an initial pH of 7.5 in organic medium. Apparent kinetic parameters (V'(max), K'(m), k'(cat) and k'(cat)/K'(m)) for the pseudoperoxidase activity were determined in organic media for different pH values from a Michaelis-Menten plot. Furthermore, the stability of the protein-calixarene complex was investigated for different initial pH values and half-life (t(1/2)) values were obtained in the range of 1.96 and 2.64 days. Hemoglobin-calixarene complex present in organic medium was recovered in fresh aqueous solutions at alkaline pH, with a recovery of pseudoperoxidase activity of over 100%. These results strongly suggest that the use of calixarene derivatives is an alternative technique for protein extraction and solubilisation in organic media for biocatalysis.

Supercritical carbon dioxide extraction of bioactive compounds from microalgae and volatile oils from aromatic plants

A discussion of the most interesting results obtained in our laboratories, during the supercritical CO(2) extraction of bioactive compounds from microalgae and volatile oils from aromatic plants, was carried out. Concerning the microalgae, the studies on Botryococcus braunii and Chlorella vulgaris were selected. Hydrocarbons from the first microalgae, which are mainly linear alkadienes (C(23)-C(31)) with an odd number of carbon atoms, were selectively extracted at 313 K increasing the pressure up to 30.0 MPa. These hydrocarbons are easily extracted at this pressure, since they are located outside the cellular walls. The extraction of carotenoids, mainly canthaxanthin and astaxanthin, from C. vulgaris is more difficult. The extraction yield of these components at 313 K and 35.0 MPa increased with the degree of crushing of the microalga, since they are not extracellular. On the other hand, for the extraction of volatile oils from aromatic plants, studies on Mentha pulegium and Satureja montana L were chosen. For the first aromatic plant, the composition of the volatile and essential oils was similar, the main components being the pulegone and menthone. However, this volatile oil contained small amounts of waxes, which content decreased with decreasing particle size of the plant matrix. For S. montana L it was also observed that both oils have a similar composition, the main components being carvacrol and thymol. The main difference is the relative amount of thymoquinone, which content can be 15 times higher in volatile oil. This oxygenated monoterpene has important biological activities. Moreover, experimental studies on anticholinesterase activity of supercritical extracts of S. montana were also carried out. The supercritical nonvolatile fraction, which presented the highest content of the protocatechuic, vanilic, chlorogenic and (+)-catechin acids, is the most promising inhibitor of the enzyme butyrylcholinesterase. In contrast, the Soxhlet acetone extract did not affect the activity of this enzyme at the concentrations tested. (C) 2011 Elsevier B.V. All rights reserved.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Interpretation of gravity data to delineate structural features connected to low-temperature geothermal resources at Northeastern Portugal

A great number of low-temperature geothermal fields occur in Northern-Portugal related to fractured rocks. The most important superficial manifestations of these hydrothermal systems appear in pull-apart tectonic basins and are strongly conditioned by the orientation of the main fault systems in the region. This work presents the interpretation of gravity gradient maps and 3D inversion model produced from a regional gravity survey. The horizontal gradients reveal a complex fault system. The obtained 3D model of density contrast puts into evidence the main fault zone in the region and the depth distribution of the granitic bodies. Their relationship with the hydrothermal systems supports the conceptual models elaborated from hydrochemical and isotopic water analyses. This work emphasizes the importance of the role of the gravity method and analysis to better understand the connection between hydrothermal systems and the fractured rock pattern and surrounding geology. (c) 2013 Elsevier B.V. All rights reserved.

A similarity measure for music signals

One of the goals in the field of Music Information Retrieval is to obtain a measure of similarity between two musical recordings. Such a measure is at the core of automatic classification, query, and retrieval systems, which have become a necessity due to the ever increasing availability and size of musical databases. This paper proposes a method for calculating a similarity distance between two music signals. The method extracts a set of features from the audio recordings, models the features, and determines the distance between models. While further work is needed, preliminary results show that the proposed method has the potential to be used as a similarity measure for musical signals.

Monte Carlo simulation of the basic features of the GE Millennium MG single photon emission computed tomography gamma camera

Objective - To describe and validate the simulation of the basic features of GE Millennium MG gamma camera using the GATE Monte Carlo platform. Material and methods - Crystal size and thickness, parallel-hole collimation and a realistic energy acquisition window were simulated in the GATE platform. GATE results were compared to experimental data in the following imaging conditions: a point source of 99mTc at different positions during static imaging and tomographic acquisitions using two different energy windows. The accuracy between the events expected and detected by simulation was obtained with the Mann–Whitney–Wilcoxon test. Comparisons were made regarding the measurement of sensitivity and spatial resolution, static and tomographic. Simulated and experimental spatial resolutions for tomographic data were compared with the Kruskal–Wallis test to assess simulation accuracy for this parameter. Results - There was good agreement between simulated and experimental data. The number of decays expected when compared with the number of decays registered, showed small deviation (≤0.007%). The sensitivity comparisons between static acquisitions for different distances from source to collimator (1, 5, 10, 20, 30cm) with energy windows of 126–154 keV and 130–158 keV showed differences of 4.4%, 5.5%, 4.2%, 5.5%, 4.5% and 5.4%, 6.3%, 6.3%, 5.8%, 5.3%, respectively. For the tomographic acquisitions, the mean differences were 7.5% and 9.8% for the energy window 126–154 keV and 130–158 keV. Comparison of simulated and experimental spatial resolutions for tomographic data showed no statistically significant differences with 95% confidence interval. Conclusions - Adequate simulation of the system basic features using GATE Monte Carlo simulation platform was achieved and validated.

An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.