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Defect detection of textiles is a necessary requirement for quality control and customer satisfaction. This paper presents a system for decision fusion in order to enhance the accuracy of defect detection in textiles. A multi-classifier decision fusion technique based on majority voting is presented to solve the problems of sensitivity to parameter variation and to make use of the advantages of the individual feature sets for accurate texture characterization. Features based on Gray Level Co-occurrence Matrix (GLCM), Laws Energy (LE) Filter, Local Binary Patterns (LBP), HU Moment invariants, Moment of Inertia (MOI) and Standard Deviation of Gray levels are used to train a set of Learning Vector Quantization (LVQ) classifiers. Detection accuracies of classifiers trained on single-feature sets are compared with those of the majority voting among the individual classifiers. The results obtained from majority voting indicate that the decision fusion technique improves the accuracy and reliability of the detection process. Empirical results indicate the high accuracy of the presented approach. The correct defect detection rate of the proposed approach is 98.64% with an average false acceptance rate of 0.0012. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel method for defect detection in homogeneous flat surface products. The coefficient of variation is used as a homogeneity measure for approximate defect localization and features extracted from the Log – Gabor filter bank response are used to accurately localize and detect the defect while reducing the complexity of Gabor based inspection approaches. The scanning window size and threshold parameter are the two major factors that affect the system performance. An adaptive technique is proposed for selecting the size of the scanning window and automating the selection of the threshold level. Compared to the Log-Gabor filters, the proposed combination resulted in speeding up the defect detection process by about ten times. The experimental results show that the proposed system gives promising results and is applicable for defect detection in homogeneous surfaces like paper, steel plates, ceramic tiles and foils. [ABSTRACT FROM AUTHOR]

The Staphylococcus aureus enterotoxin A (SEA) is the toxin mostly involved in Staphylococcus aureus (S. aureus) food poisoning. In this study, the effect of different lactic acid (LA) concentrations (LA 1% and 2%) and temperatures (4°C, 25°C, and 37 °C) on S. aureus growth and relative sea expression in fresh meat cuts were studied. Real-Time RT-PCR used to determine the relative sea expression. Fresh meat cuts were inoculated with 105 CFU/g of S. aureus producing enterotoxin A. S. aureus growth and relative sea expression were regularly tested for 48 hours. The growth of S. aureus was decreased by one log CFU/g than control sample using 1% LA and 2% LA 2% (5.32 ± 3.76 log CFU/g, 4.38 ± 3.00 log CFU/g and 4.54 ± 3.18 log CFU/g respectively) at zero time. Relative expression of the sea gene in both LA concentrations was lower than control. Moreover, both lactic acid concentrations had effect on relative sea gene expression at all examined hours, especially at 4°C compared to control samples. The higher the lactic acid concentration, the lower the S. aureus entero- toxin A relative expression was. [ABSTRACT FROM AUTHOR]

Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R 2  = 1.00 outperforming the other two approaches with R 2  = 0.80 for DT and R 2  = 0.76 for SVM., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The last decade has witnessed an explosive use of medical images and Electronics Patient Record (EPR) in the healthcare sector for facilitating the sharing of patient information and exchange between networked hospitals and healthcare centers. To guarantee the security, authenticity and management of medical images and information through storage and distribution, the watermarking techniques are growing to protect the medical healthcare information. This paper presents a technique for embedding the EPR information in the medical image to save storage space and transmission overheads and to guarantee security of the shared data. In this paper a new method for protecting the patient information in which the information is embedded as a watermark in the discrete wavelet packet transform (DWPT) of the medical image using the hospital logo as a reference image. The patient information is coded by an error correcting code (ECC), BCH code, to enhance the robustness of the proposed method. The scheme is blind so that the EPR can be extracted from the medical image without the need of the original image. Therefore, this proposed technique is useful in telemedicine applications. Performance of the proposed method was tested using four modalities of medical images; MRA, MRI, Radiological, and CT. Experimental results showed no visible difference between the watermarked and the original image. Moreover, the proposed watermarking method is robust against a wide range of attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, contrast adjustment, and sharpen filter and rotation.

Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate., (Copyright 1999 Academic Press.)