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

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

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals GLCM – SVM based Classification of Brain MRI with K-Means Clusters

2020 ◽  
Vol 9 (3) ◽  
pp. 100-104
Keyword(s):  
Brain Mri ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Selection Approach ◽  
Random Manner ◽  
Feature Selection Approach ◽  
Occurrence Matrix ◽  
Mr Brain

In this proposed method, MR Brain image segmentation technique based on K-means clustering combined with Discrete Wavelet Transform (DWT) based feature extraction and Gray Level Co-Occurrence Matrix (GLCM) based feature selection approach has been presented. A Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier has been selected for this process. The Performance of the classifier was estimated through accuracy based on the fractions selectivity and sensitivity. Accuracy of the proposed classifier was found to be 93%. Moreover, in this proposed method, instead of selecting the cluster centres in a random manner, Histogram technique was used.

scholarly journals Automatic Target Recognition (ATR) from SAR Imaginary by Using Machine Learning Techniques

2020 ◽  
Author(s):  
Umut Ozkaya
Keyword(s):  
Machine Learning ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Sar Images ◽  
Gaussian Kernels ◽  
Testing Stage

Automatic Target Recognition (ATR) in Synthetic aperture radar (SAR) images becomes a very challenging problem owing to containing high level noise. In this study, a machine learning-based method is proposed to detect different moving and stationary targets using SAR images. First Order Statistical (FOS) features were obtained from Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on gray level SAR images. Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and Gray Level Size Zone Matrix (GLSZM) algorithms are also used. These features are provided as input for the training and testing stage Support Vector Machine (SVM) model with Gaussian kernels. 4-fold cross-validations were implemented in performance evaluation. Obtained results showed that GLCM + SVM algorithm is the best model with 95.26% accuracy. This proposed method shows that moving and stationary targets in MSTAR database could be recognized with high performance.

Automatic detection of ischemic stroke using higher order spectra features in brain MRI images

2019 ◽  
Vol 58 ◽  
pp. 134-142 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Kristen M. Meiburger ◽  
Oliver Faust ◽  
Joel En Wei Koh ◽  
Shu Lih Oh ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Brain Mri ◽  
Automatic Detection ◽  
Higher Order ◽  
Higher Order Spectra

scholarly journals A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shakhawan H. Wady ◽  
Raghad Z. Yousif ◽  
Harith R. Hasan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet

Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

scholarly journals Grain Surface Classification via Machine Learning Methods

2020 ◽  
Author(s):  
Hüseyin Duysak ◽  
Umut Ozkaya ◽  
Enes Yiğit
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Complex Form ◽  
Extraction Process ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Grain Surface

In this study, radar signals were analyzed to classify grain surface types by using machine learning methods. Radar backscatter signals were recorded using a vector network analyzer between 18-40 GHz. A total of 5681 measurements of A scan signals were collected. The proposed method framework consists of two parts. First Order Statistical features are obtained by applying Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) on backscatter signals in the first part of the framework. Classification process of these features was carried out with Support Vector Machine (SVM). In the second part of the proposed framework, two dimensional matrices in complex form were obtained by applying Short Time Fourier Transform (STFT) on the signals. Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) were obtained and feature extraction process was completed. Classification process was carried out with DVM. 10-k cross validation was applied. The highest performance was achieved with STFT+GLCM+SVM.

scholarly journals A Novel Framework for Detection of Cervical Cancer

2018 ◽  
Vol 7 (2) ◽  
pp. 26-30
Author(s):  
V. Pushpalatha
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Curvelet Transform ◽  
Uterine Cervical Cancer ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Adaptive Support ◽  
Occurrence Matrix ◽  
Five Phases

Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

scholarly journals Classification of Liver Diseases Based on Ultrasound Image Texture Features

2019 ◽  
Vol 9 (2) ◽  
pp. 342 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chun-Chao Chang ◽  
Chien-Tien Su ◽  
Pham Quoc Phu
Keyword(s):  
Liver Cancer ◽  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Liver Malignancy

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.

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