Training spiking neural networks with the improved Grey-Level Co-occurrence Matrix algorithm for texture analysis

The gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

Spinal curvature type can be detected from digital X-ray images. Experts diagnose spinal curvature for a long time to obtain accurate results. This research aims to analyze the use of image processing techniques to extract features in two types of spinal imagery, normal and abnormal (i.e., scoliosis), by applying the Gray Level Co-occurrence Matrix (GLCM) algorithm and Support Vector Machine (SVM) for the classification method. This study used 40 images divided into 4 data sets for analysis. Three distance parameters, 50, 75, and 100 pixels, and three parameters of quantization values, 8, 16, and 32, were utilized for analysis. The highest accuracy obtained from one of the specific data set was 100%, while the highest accuracy of the average of each value distance and quantization was 90%. The GLCM algorithm could differentiate the abnormality of spinal imagery.

Abstract: Pomegranate is a valuable fruit crop that is widely cultivated in various regions. However, its production is often threatened by various diseases that can significantly affect its yield and quality. In this project, we propose a method for pomegranate disease detection and pesticides suggestion using the gray-level co-occurrence matrix (GLCM) algorithm. The proposed approach utilizes GLCM to extract texture features from the pomegranate leaf images and classify them into healthy or diseased categories. Moreover, we suggest the most effective pesticides to control the detected diseases by using the GLCM algorithm. The performance of the proposed method is evaluated using a dataset of pomegranate leaf images with different disease symptoms. The experimental results demonstrate the effectiveness of our approach in detecting and classifying pomegranate diseases with high accuracy. Furthermore, the suggested pesticides show promising results in controlling the diseases. Overall, the proposed method can assist farmers in identifying pomegranate diseases at an early stage and provide appropriate suggestions for pesticide selection, which can reduce crop losses and enhance crop productivity. Keywords: classification, disease detection, GLCM algorithm.

QR image feature extraction effectiveness based on metrics using spectral clustering and grey level Co-Occurrence matrix algorithm

Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

Alzheimer’s disease (AD) is a neurological, irreversible disorder of the brain that reduces the cognitive skills and memory ability. Alzheimer’s Disease should be diagnosed in initial phase for providing efficacious treatment to the patient. Delay in treatment may lead to the demise of patient. This paper presents a new technique for feature extraction in Alzheimer’s Disease classification. Dual Tree complex wavelet transform is applied to decompose the image into six sub images. Subsequently, Gray Level Co-occurrence Matrix is calculated from six sub images. From these matrices 22 statistical features are obtained for each sub-band image. Further, Genetic Algorithm is used to obtain the relevant and optimum features to improve the classification accuracy. Dual Tree Complex Wavelet Transform shows high directional selectivity, so it can decompose the image from six different directions and provide additional informative features. In addition, Gray Level Co¬occurrence Matrix deals with the image intensity directly, so it provides the information related with the patterns which are mutually occurring. Genetic Algorithm requires less information and provides more solutions, so it extracts optimum features and improves classification accuracy. The proposed method obtained 96.2% accuracy, 96.4% sensitivity and 95.9% specificity. The performance of the proposed work surpasses the existing traditional approaches for AD detection.

Experimental analysis on relationship between roughness parameters and texture features of 6082T6 sandblasted components

Label-free and rapid classification of cells can have awide range of applications in biology. We report a robust method of polarization diffraction imaging flow cytometry (p-DIFC) for achieving this goal. Coherently scattered light signals are acquired from single cells excited by a polarized laser beam in the form of two cross-polarized diffraction images. Image texture and intensity parameters are extracted with a gray level co-occurrence matrix (GLCM) algorithm to obtain an optimized set of feature parameters as the morphological "fingerprints" for automated cell classification. We selected the Jurkat T cells and Ramos B cells to test the p-DIFC method's capacity for cell classification. After detailed statistical analysis, we found that the optimized feature vectors yield accuracies of classification between the Jurkat and Ramos ranging from 97.8% to 100% among different cell data sets. Confocal imaging and three-dimensional reconstruction were applied to gain insights on the ability of p-DIFC method for classifying the two cell lines of highly similar morphology. Based on these results we conclude that the p-DIFC method has the capacity to discriminate cells of high similarity in their morphology with "fingerprints" features extracted from the diffraction images, which may be attributed to subtle but statistically significant differences in the nucleus-to-cell volume ratio in the case of Jurkat and Ramos cells.

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed texture analysis method was utilized to distinguish different images of Arabidopsis microtubules. The results showed that the effect of BEMD algorithm on edge preserving accompanied with noise reduction was positive, and the geometrical characteristic of the texture was obvious. Four texture parameters extracted by GLCM perfectly reflected the different arrangements between the two images of cortical microtubules. In summary, the results indicate that this method is feasible and effective for the image quantitative analysis of plant cortical microtubules. It not only provides a new quantitative approach for the comprehensive study of the role played by microtubules in cell life activities but also supplies references for other similar studies.

Research has been conducted on detecting tuberculosis (TB) using machine learning. In this study, chest X-ray (CXR) image data was used with a pixel value of 512 x 512 and PNG format consisting of normal lung images and TB-infected lung images in a 50:50 ratio; the number of images was 200 training data images and 80 testing data images. In the preprocessing stage, grayscaling is carried out so the image has a grayscale. Then, do the image improvement using contrast stretching. Furthermore, image extraction was carried out using 22 GLCM features with variations in the direction of the angles of 0°, 45°, 90°, and 135°. The result of feature extraction data is then identified using KNN Classification. The training results have the highest accuracy value with variations in the direction of the GLCM angle of 45° and the value of K = 3; at the testing stage, it produces an accuracy of 90%.

Relationship of CT Radiomics and Dose Texture to Radiation-Induced Swallow Dysfunction in Head and Neck Cancer

International audience

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.