Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.

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Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic

Complexity ◽

10.1155/2021/3511375 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Nhat-Duc Hoang ◽

Thanh-Canh Huynh ◽

Van-Duc Tran

Keyword(s):

Computer Vision ◽

Asphalt Pavement ◽

Classification Model ◽

Image Texture ◽

Rank Test ◽

Support Vector ◽

Signed Rank ◽

Classification Boundary ◽

Signed Rank Test ◽

Class Labels

During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine

Advances in Civil Engineering ◽

10.1155/2020/4190682 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Nhat-Duc Hoang ◽

Quoc-Lam Nguyen

Keyword(s):

Differential Evolution ◽

Texture Analysis ◽

Concrete Surface ◽

Image Texture ◽

Support Vector ◽

Image Texture Analysis ◽

Promising Alternative ◽

Adaptive Differential Evolution ◽

Hybrid Intelligence ◽

Intelligence Approach

To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.

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Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18158059 ◽

2021 ◽

Vol 18 (15) ◽

pp. 8059

Author(s):

Oliver Faust ◽

Joel En Wei Koh ◽

Vicnesh Jahmunah ◽

Sukant Sabut ◽

Edward J. Ciaccio ◽

...

Keyword(s):

Brain Mri ◽

Higher Order ◽

Image Texture ◽

Support Vector ◽

Discrete Wavelet ◽

Stroke Severity ◽

Gray Level ◽

Anterior Circulation ◽

Severity Classification ◽

Higher Order Spectra

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, SPE

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AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX

Journal of Mechanics in Medicine and Biology ◽

10.1142/s021951941640008x ◽

2016 ◽

Vol 16 (01) ◽

pp. 1640008 ◽

Cited By ~ 3

Author(s):

U. RAJENDRA ACHARYA ◽

HAMIDO FUJITA ◽

SHREYA BHAT ◽

JOEL EW KOH ◽

MUHAMMAD ADAM ◽

...

Keyword(s):

Probabilistic Neural Network ◽

Characteristic Curve ◽

Gaussian Mixture ◽

The Body ◽

Rank Test ◽

Support Vector ◽

K Nearest Neighbor ◽

Bhattacharyya Distance ◽

Noninvasive Technique ◽

Distance Information

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.

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Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Computational Intelligence and Neuroscience ◽

10.1155/2019/8097213 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Nhat-Duc Hoang ◽

Van-Duc Tran

Keyword(s):

Image Processing ◽

Image Texture ◽

Rank Test ◽

Support Vector ◽

Gray Level ◽

Pipe Surface ◽

Pipe System ◽

Building Maintenance ◽

Promising Tool ◽

Occurrence Matrix

To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.

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Handling Water Reflections for Computer Vision in Aquaculture

Transactions of the ASABE ◽

10.13031/trans.12466 ◽

2018 ◽

Vol 61 (2) ◽

pp. 469-479 ◽

Cited By ~ 4

Author(s):

Chao Zhou ◽

Chuanheng Sun ◽

Kai Lin ◽

Daming Xu ◽

Qiang Guo ◽

...

Keyword(s):

Image Processing ◽

Computer Vision ◽

Support Vector Machine ◽

Classification Accuracy ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Image Texture ◽

Support Vector ◽

Type I

Abstract. In aquaculture, almost all images collected of an aquaculture scene contain reflections, which often affect the results and accuracy of machine vision. Classifying these images and obtaining images of interest are key to subsequent image processing. The purpose of this study was to identify useful images and remove images that had a substantial effect on the results of image processing for computer vision in aquaculture. In this study, a method for classification of reflective frames based on image texture and a support vector machine (SVM) was proposed for an actual aquaculture site. Objectives of this study were to: (1) develop an algorithm to improve the speed of the method and to ensure that the method has a high classification accuracy, (2) design an algorithm to improve the intelligence and adaptability of the classification, and (3) demonstrate the performance of the method. The results show that the average classification accuracy, false positive rate, and false negative rate for two types of reflective frames (type I and II) were 96.34%, 4.65%, and 2.23%, respectively. In addition, the running time was very low (1.25 s). This strategy also displayed considerable adaptability and could be used to obtain useful images or remove images that have substantial effects on the accuracy of image processing results, thereby improving the applicability of computer vision in aquaculture. Keywords: Aquaculture, Genetic algorithm, Gray level-gradient co-occurrence matrix, Principal component analysis, Reflection frame, Support vector machine.

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Does daily wear time of Twin Block reliably predict its efficiency of class II treatment?

Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie ◽

10.1007/s00056-021-00300-7 ◽

2021 ◽

Author(s):

Michal Sarul ◽

Marek Nahajowski ◽

Grzegorz Gawin ◽

Joanna Antoszewska-Smith

Keyword(s):

Statistical Significance ◽

Characteristic Curve ◽

Class Ii ◽

Class I ◽

Rank Test ◽

Treatment Efficiency ◽

Significance Level ◽

Twin Block ◽

Wear Time ◽

Class Ii Treatment

Abstract Purpose The objective of this study was to investigate how daily wear time (DWT) influences class II malocclusion treatment efficiency. Materials and methods The study group consisted of 55 patients (mean age 10.4 years) diagnosed with a class II/1 malocclusion. Twin block appliances, with built-in Theramon® microsensors (MC Technology, Hargelsberg, Austria) to monitor patients’ cooperation (daily wear time assessment), were used for treatment. Cephalograms were taken and the following initial and final measurements were compared: Co-Gn, Co-Go, Co-Olp, Pg-Olp, WITS, SNA, SNB, ANB, Co-Go-Me, overjet, molar and canine relationships. The Shapiro–Wilk test, Wilcoxon signed-rank test, Student’s t-test, Levene’s test, Mann–Whitney U test, Kruskal–Wallis test, χ2 test, and Spearman’s rank correlation coefficient with p < 0.05 set as the statistical significance level were used to determine the correlation of the outcomes with DWT; a ROC (receiver operating characteristic) curve was calculated to illustrate diagnostic ability of the binary classifier system. Results DWT was very highly positively correlated with change of the Pg-Olp parameter and highly with an improvement in the ANB, SNA, and SNB angles, an increase in the WITS parameter and an increase in Co-Gn distance. DWTs < 7.5 h correlated with significantly less improvement of the investigated variables. However, DWT > 7.5 h did not significantly correlate with the improvement of the overjet and most of the linear parameters in the mandible. The ROC curve and its AUC (area under curve) allowed the determination of a DWT of 7 h and 48 min to be capable of establishing a class I relationship with 83% probability. Conclusions Class II treatment efficiency was influenced by DWT; an 8 h threshold value had an 83% probability of establishing a class I relationship.

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Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines

Journal of Personalized Medicine ◽

10.3390/jpm11030199 ◽

2021 ◽

Vol 11 (3) ◽

pp. 199

Author(s):

Fajar Javed ◽

Syed Omer Gilani ◽

Seemab Latif ◽

Asim Waris ◽

Mohsin Jamil ◽

...

Keyword(s):

Low Income ◽

Operating Characteristic ◽

Mental Health Problems ◽

Characteristic Curve ◽

Antenatal Depression ◽

Low Income Countries ◽

Support Vector ◽

Depression And Anxiety ◽

Gynecology And Obstetrics ◽

Operating Characteristic Curve

Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.

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An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Ultrasonic Imaging ◽

10.1177/0161734621998091 ◽

2021 ◽

pp. 016173462199809

Author(s):

Dhurgham Al-karawi ◽

Hisham Al-Assam ◽

Hongbo Du ◽

Ahmad Sayasneh ◽

Chiara Landolfo ◽

...

Keyword(s):

Gabor Filter ◽

Empirical Evaluation ◽

Texture Features ◽

Image Texture ◽

Support Vector ◽

Simple Majority ◽

Ultrasound Scan ◽

Histograms Of Oriented Gradients ◽

The Individual ◽

Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Sensors ◽

10.3390/s21134283 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4283

Author(s):

Md.-Habibur Rahman ◽

Md. Shahjalal ◽

Moh. Khalid Hasan ◽

Md.-Osman Ali ◽

Yeong-Min Jang

Keyword(s):

Light Emitting Diode ◽

Characteristic Curve ◽

Analysis Data ◽

Image Sensor ◽

Data Rate ◽

Support Vector ◽

Svm Classifier ◽

High Data ◽

Receiver System ◽

Optical Camera Communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

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