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1

Machine vision-based surface defect detection method for welds

Xu Su, Cui Wenxuan, Zhou Xianzhang, Zhong Qiucheng, Wei Yifan, Wang Yipeng

Diagnostyka

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2025

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Vol. 26, No. 1

art. no. 2025106

EN

The texture feature extraction including grayscale co-occurrence matrix and various shape feature extraction methods are adopted in this paper, as well as convolutional neural network based on Visual Geometry Group-16 structure. In particular, the Squeeze-and-Excitation module and dilated convolution technique are introduced to improve the model, aiming to enhance its feature extraction and classification capabilities. On the JPEGWELD dataset, the improved model had 98.7% accuracy in the training set, 97.9% accuracy in the test set, and 98.7% recall rate. In the comparative analysis, although the number of parameters of the improved VGG16 model was 33.64M and the maximum model size was 385MB, the detection time was only 1.3s. The results demonstrated that the model had efficient optimization and computational performance, with a good balance between design and optimization while maintaining a short detection time. The proposed method exhibits high accuracy and efficiency in the detection of various types of weld defects, demonstrating strong universality and adaptability. Its applicability to diverse industrial settings is evident. The study provides an effective solution for industrial automated inspection, which is of great significance to improve the quality control level and production efficiency of manufacturing industry.

2

Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection

Ezazi Yasamin, Ghaderyan Peyvand

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

950--962

EN

Background: Parkinson’s disease (PD) detection holds great potential for providing effective treatments, slowing the disease process, and improving the quality of patient’s life, but the development of a clinical accurate, generalized, robust and cost-effective method is a challenge. Method: In this paper, a novel PD detection method based on textural features of clinical electroencephalogram (EEG) signals has been proposed. In contrast to most existing methods, which do not consider reward positivity (RP)-relevant features for automatic PD detection, this method has focused on providing a novel EEG marker of RP using an enhanced time-frequency representation, texture descriptors based on Gray Level Co-occurrence Matrix, local binary pattern, and sparse coding classifier. Results: The proposed method has been evaluated using EEG signals recorded during a reinforcement-learning task from 28 patients with PD and 28 sex- and age matched healthy controls. Results have demonstrated that the proposed architecture reaches a high detection with an average accuracy rate of 100%, presenting better performance and outperforming previous techniques. Conclusions: it can provide a new solution to detect RP changes in PD and can offer obvious stability advantages on several clinical and technical variables (medication states, type of textural descriptors, reduced channels), suggesting a generalizable detection system.

3

Gray-level co-occurrence matrix of Fourier synchrosqueezed transform for epileptic seizure detection

Mamli Shamzin, Kalbkhani Hashem

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

87--99

EN

Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (d), theta (u), alpha (a), beta (b), and gamma (g). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.

4

A classification framework for prediction of breast density using an ensemble of neural network classifiers

Kumar I., Bhadauria H. S., Virmani J., Thakur S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

217--228

EN

The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying interpixel distance 'd' from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [PB-I PB-II PB-III PB-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. [...]

5

Hierarchical classification of normal, fatty and heterogeneous liver diseases from ultrasound images using serial and parallel feature fusion

Alivar A., Danyali H., Helfroush M. S.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

697--707

EN

This study presents a computer-aided diagnostic system for hierarchical classification of normal, fatty, and heterogeneous liver ultrasound images using feature fusion techniques. Both spatial and transform domain based features are used in the classification, since they have positive effects on the classification accuracy. After extracting gray level co-occurrence matrix and completed local binary pattern features as spatial domain features and a number of statistical features of 2-D wavelet packet transform sub-images and 2-D Gabor filter banks transformed images as transform domain features, particle swarm optimization algorithm is used to select dominant features of the parallel and serial fused feature spaces. Classification is performed in two steps: First, focal livers are classified from the diffused ones and second, normal livers are distinguished from the fatty ones. For the used database, the maximum classification accuracy of 100% and 98.86% is achieved by serial and parallel feature fusion modes, respectively, using leave-one-out cross validation (LOOCV) method and support vector machine (SVM) classifier.