Surface roughness analysis from GLCM features: a surrogate speckle interferometric method

Bridging the gap between micro- and macro-scales in medical imaging with textural analysis – A biological basis for CT radiomics classifiers?

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

The analysis of anti-nuclear antibodies in HEp-2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.

This study proposes a novel method that uses electroencephalography (EEG) signals to classify Parkinson's Disease (PD) and demographically matched healthy control groups. The method utilizes the reduced beta activity and amplitude decrease in EEG signals that are associated with PD. The study involved 61 PD patients and 61 demographically matched controls groups, and EEG signals were recorded in various conditions (eyes closed, eyes open, eyes both open and closed, on-drug, off-drug) from three publicly available EEG data sources (New Mexico, Iowa, and Turku). The preprocessed EEG signals were classified using features obtained from gray-level co-occurrence matrix (GLCM) features through the Hankelization of EEG signals. The performance of classifiers with these novel features was evaluated using extensive cross-validations (CV) and leave-one-out cross-validation (LOOCV) schemes. This method under 10 × 10 fold CV, the method was able to differentiate PD groups from healthy control groups using a support vector machine (SVM) with an accuracy of 92.4 ± 0.01, 85.7 ± 0.02, and 77.1 ± 0.06 for New Mexico, Iowa, and Turku datasets, respectively. After a head-to-head comparison with state-of-the-art methods, this study showed an increase in the classification of PD and controls.

The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan-Meier analysis. This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

This paper proposes new computational strategies to improve optical coherence tomography image quality for age-related macular degeneration identification. Integrated into an age-related macular degeneration detection system, these algorithms automate and improve the identification of abnormalities in optical coherence tomography images, aiding in the classification of normal and abnormal macular tissues. The research presents an innovative approach to detecting age-related macular degeneration related anomalies, combining texture analysis, statistical evaluation, and genetic algorithms for feature selection. Genetic algorithm optimization finds the best predictive characteristics by using in-depth texture analysis with the gray level co-occurrence matrix and comprehensive statistical research. Gray level co-occurrence matrix features are analyzed at four angles (0°, 45°, 90°, and 135°), with the random forest classifier trained using optimized features. The random forest classifier plays a vital role in both the training and testing phases, achieving no-table results: an error rate of 0% for selected features, 1.9% for all features, and 7.5% for no features, and an overall system classification accuracy of 100% for training data for all, while maintaining 92.458% 98.113%, 100% for testing data of no features, all features and selected features.

Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values ​​in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%.

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.

We established prediction models based on the combination of spectral and different advanced image features to improve the prediction accuracy of solid-soluble content (SSC) of apple. Eight optimal wavelengths were selected using a new variable selection method called variable combination population analysis (VCPA). Image textural features of the first three principal component score images were obtained using a gray level co-occurrence matrix (GLCM) and a local binary pattern (LBP). Next, a random frog algorithm was developed to select optimal textural features for further analysis. A support vector regression (SVR) model based on spectral and different textural features was developed to predict the SSC of the apple. The model based on eight optimal wavelengths and nine optimal GLCM features of principal component images yielded the best result with the determination coefficient for prediction (Rp2) of 0.9193, root mean square error for prediction (RMSEP) of 0.2955, and the ratio of the standard deviation of the prediction set to the root mean square error of prediction (RPD) with a value of 3.50. These results revealed that the spectral combined with optimal GLCM features from principal component images coupled with the SVR model has the potential for prediction of the SSC of apple.

The agricultural sector is negatively impacted by plant diseases. This illness reduces yield productivity and causes farmers to lose a lot of money. It is critical to identify illnesses in plants in order to conserve agricultural crop output and reduce pesticide use. The study presents image classification using the n-ary Thepade's Sorted Block Truncation Coding (TSBTC) with the Gray Level Cooccurrence Matrix(GLCM) for rice leaf disease detection. The database used here is “Rice Leaf Disease Identification,” downloaded from Kaggle, containing 3334 images with four rice leaf disease categories, namely hispa, leafblast, brownspot, and healthy. Various degrees of feature vectors are generated by adopting a fusion of TSBTC and GLCM. The classification algorithms belonging to different families are used for image classification from Functions, Trees, and Bayes families. The performance of the proposed method is examined using classification accuracy. The ensemble of random forest, random tree, and J48 classifier using a fusion of Thepade's SBTC and GLCM features has performed better in comparison to other methods studied.

Brain tumors are a growth of abnormal cells in the intracranial tissue that can disrupt proper brain function. In general, brain tumors are classified into two main categories, benign and malignant. This research aims to classify three types of benign tumors, that are Meningioma (Mg), Glioma (Gl), and Pituitary (Pt) from MRI images. The benign tumors types are classified into four data categories, that are Mg-Gl, Mg-Pt, Gl-Pt, Mg-GI-Pt. The Feature extraction uses Discrete Wavelet Transform (DWT) and Gray Level Co-Occurrence Matrix (GLCM) variant combination as a hybrid feature for recognize and classifying benign tumors types. The classification uses Convolutional Neural Network (CNN) method with ten layers structure. From our experiments, the average accuracy value of DWT combined with four GLCM features, that are Contrast, Homogeneity, Correlation, and Energy is 78.03% in all data categories.

For the radiologists, it is difficult to identify the mass on a mammogram since the masses are surrounded by the mammary gland and blood vessel. In current breast cancer screening, about 10 by radiologists owing to the ambiguous margins of lesions and the visual fatigue of radiologists resulting from the long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions that may indicate the presence of breast cancer. The purpose of this study is to construct an automated CADe system using a new feature extraction method for mammographic mass detection. In this system, some adaptive square regions of interest (ROIs) are segmented according to the size of suspicious areas. Then a new feature extraction method adopting grey level co-occurrence matrix and optical density features called GLCM-OD features is applied to each ROI. The GLCM-OD features describe local grey level texture characteristics and the whole photometric distribution of the ROI. Finally, the stepwise linear discriminant analysis is applied to classify abnormal regions by selecting and rating individual performance of each feature. This system is trained and tested by 358 mammographic cases from the digital database of screening mammography (DDSM). The proposed system averagely provides sensitivity of 97.3% with 4.9 false positives per image and the Az is 0.981. The results prove that the proposed system achieves satisfactory detection performance.Keywordscomputer-aided detection systemgrey level co-occurrence matrixoptical densityGLCM-OD features