Spatial Gray Level Dependence Method Research Articles

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning.

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

Image tampering detection using genetic algorithm

As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.

Computer aided breast cancer diagnosis: an SVM-based mammogram classification approach

This paper addresses the problem of mammogram classification (benign vs. malignant) through the utilisation of the state-of-the-art machine learning paradigm of support vector machines (SVMs). We are particularly interested in evaluating the discrimination efficiency of a set of feature extraction algorithms that have been proposed in the relevant literature for describing the textual characteristics present in mammogram masses. Our research focuses on comparing the classification accuracy associated with two well-established feature generation methodologies, namely, spatial grey level dependence method (SGLDM) and run difference method (RDM). Our experimentation was conducted on a publicly available mammogram database by parameterising the underlying kernel function of SVMs on different subsets of features. Our results indicate a moderately high classification accuracy for the linear SVM classifier when trained on the complete set of features.

Computer aided breast cancer diagnosis: an SVM-based mammogram classification approach

This paper addresses the problem of mammogram classification (benign vs. malignant) through the utilisation of the state-of-the-art machine learning paradigm of support vector machines (SVMs). We are particularly interested in evaluating the discrimination efficiency of a set of feature extraction algorithms that have been proposed in the relevant literature for describing the textual characteristics present in mammogram masses. Our research focuses on comparing the classification accuracy associated with two well-established feature generation methodologies, namely, spatial grey level dependence method (SGLDM) and run difference method (RDM). Our experimentation was conducted on a publicly available mammogram database by parameterising the underlying kernel function of SVMs on different subsets of features. Our results indicate a moderately high classification accuracy for the linear SVM classifier when trained on the complete set of features.

Comparison of Seven Texture Analysis Indices for Their Applicability to Stereological Correction of Mineral Liberation Assessment in Binary Particle Systems

An effective correction method for stereological bias is required because of the importance of accurate assessment of mineral liberation of ore particles. Stereological bias is error caused by the estimation of a three-dimensional liberation state based on two-dimensional sectional measurements. Recent studies have proposed a stereological correction method based on sectional particle texture analysis, which employs numerical particle models. However, the applicability of this method to unfamiliar particle systems, with different shape and texture characteristics from the numerical particle model, has not been thoroughly investigated. In this study, the viability of the method for examination of the internal structure and shape of unfamiliar particles, was assessed using four types of particle systems, based on combinations of two types of internal structures (Boolean and Voronoi) and two types of particle shapes (spherical and irregular). Seven different texture analysis indices were utilized for composition distribution correction with regard to each of the four types of particle systems. The results suggested that a model based on the angular second moment and/or entropy, employed by the spatial gray level dependence method, showed the greatest viability for assessment of unfamiliar particle internal structure and/or shape.

Macrostructural Biofilm Characterization via Textural Image Analysis by SGLDM and GLRLM

Abstract The macrostructure development of biofilms grown in an annular reactor and in a rotating biological contactor has been monitored using the Spatial Gray-level Dependence Method (SGLDM) and the Gray-Level Run Length Method (GLRLM). Due to the large number of descriptors, Principal Components Analysis was used to sort the results. Both methods look promising for biofilm monitoring.

Statistical Image Analysis of Psychological Projective Drawings

We propose statistical image analysis for psychological projective drawings to facilitate assessing the reliability of the projective drawing making the determination of its validity difficult. Standard analysis involves (1) drawing a picture, (2) scanning the drawing, (3) dividing the drawing, (4) analyzing the gray level histogram moment (GLHM), (5) applying spatial gray level dependence method (SGLDM), (6) applying the gray level difference method (GLDM) for the drawing, and (7) interpreting the drawing. To demonstrate the proposed procedure, we used the tree test (Baum test). Three adults were presented with blank A4 paper and asked to draw a picture of a tree with fruit on it. Drawings were analyzed by statistical image analysis and results interpreted clinically.

描画から『心理』を解釈する樹木テストの画像解析と臨床心理学的解釈

An image analysis method for projective Tree Test was utilized to interpret psychological process of artists and university students. The procedure was as follow: (1) drawing a picture, (2) scanning the drawing picture, (3) dividing the drawing picture, (4) the gray level histogram moment (GLHM) analysis, (5) the spatial gray level dependence method (SGLDM) analysis, (6) the gray level difference method (GLDM) analysis for the drawing picture, and (7) interpretation of the drawing picture. Ink painting drawn by two famous Japanese artists, Seshu and Kohrin were firstly analyzed. Secondly, the tree pictures drawn by three undergraduate students were analyzed. Results were interpreted from the clinical psychological point of view.

Diagnosis of Solitary Lung Nodule Using Texture e Geometry in Computerized Tomography Images: Preliminary Results

This paper analyzes measures based on texture by means of the Spatial Gray Level Dependence Method, and geometry using nodules skeleton, with the purpose of characterizing solitary lung nodules as malignant or benign in computerized tomography images. Based on a sample of 31 nodules, 25 benign and 6 malignant, these methods are first analyzed individually and then jointly, with classification and analysis techniques (linear stepwise discriminant analysis, leave-one-out and ROC curve). We have concluded that the individual measures and their combinations produce good results in the diagnosis of solitary lung nodules. Nevertheless, there is the need to perform tests with a larger database and more complex cases in order to obtain a more precise behavior pattern.

Partial discharge classification using principal component transformation

Texture analysis algorithms have been applied to generate different features for identifying partial discharge (PD) sources. The algorithms utilised are the spatial gray-level dependence method, gray-level difference histogram method, gray-level run-length method and the power spectrum method. To reduce the identification time, it is important to minimise the number of features used to describe the PD sources in the feature space. Principal component transformation has been applied as a feature reduction technique on the features obtained from the texture analysis algorithms. The classification accuracy of the principal components generated has been established using a minimum-distance classifier for six different types of PD sources created in the laboratory as well as for a practical case simulating two types of PD on an actual cable length.

Time and space results of dynamic texture feature extraction in MR and CT image analysis.

Texture feature extraction is a fundamental part of texture image analysis. Therefore, the reduction of its computational time and storage requirements should be an aim of continuous research. The Spatial Grey Level Dependence Method (SGLDM) is one of the most important statistical texture description methods, especially in medical image analysis. Co-occurrence matrices are employed for the implementation of this method; however, they are inefficient in terms of computational time and memory space, due to their dependency on the number of gray levels (gray-level range) in the entire image. Since texture is usually measured in a small image region, a large amount of memory is wasted while the computational time of the texture feature extraction operations is unnecessarily raised. Their inefficiency puts up barriers to the wider utilization of SGLDM in a real application environment, such as a clinical environment. In this paper, the memory space and time efficiency of a dynamic approach to texture feature extraction in SGLDM is investigated through a pilot application in the analysis of magnetic resonance (MR) and computed tomography (CT) images.

Feature analysis for scaled and rotated texture segmentation

In this paper a class of spatial and spectral features obtained from the most popular methods for texture classification are analyzed for their ability to segment scaled and rotated textures. The features are defined from: the co-occurrence matrix using spatial-gray level dependence method, the gray level run length method, the spatial gray level difference method, the Fourier spectrum and the Gabor transform. Several experimental results are presented and discussed. The Gabor features and the co-occurrence matrix features give best results followed by the pixel-based, Fourier and spatial gray level difference features. The run length features perform poorly. The feature analysis shows that the angle at which the second order statistical features such as the co-occurrence features and gray level difference features are obtained, control the performance of the features. The Gabor features gives best results when the filter is tuned well to the orientation of the textured images. The features can be used for segmenting images obtained at different resolutions and transformations by augmenting the statistical invariance of the features to scale and rotation. Contrary to earlier conclusions, the analysis of these feature sets for scaled and rotated texture segmentation proves that some of the features can very well be applied to practical situations and further enhanced for better performance.

Textural neural network and version space classifiers for remote sensing

This paper presents a study of neural networks and version spaces for classification of remote sensing data. In the first network, precomputed textures based on the Spatial Grey Level Dependence (SGLD) method are fed to the net in conjunction with the spectral data. The second system is the sliding window network which uses all pixels in a small neighbourhood for classification of the central pixel. The third system is based on the candidate elimination implementation of the version space method for concept acquisition and is shown to achieve a performance similar to that of the neural systems but with faster training and symbolic rule generation.

Textural Parameters Based on the Spatial Gray Level Dependence Method Applied to Melanocytic Nevi

The purpose of this study is to evaluate the Spatial Gray Level Dependence Method (SGLDM) of texture analysis with respect to its ability to discriminate between melanocytic nevi and normal skin. Thirteen textural features based on the SGLDM were evaluated with respect to their relative sensitivities to both texture and tone. Ten features were found to be more sensitive to texture than tone and were selected for further study. Twenty-four digitized images of benign melanocytic nevi were obtained from six volunteers. Ten textural features were analyzed for each nevus and for surrounding sections of normal skin. Of these 10 features, 8 features can distinguish between textural properties of melanocytic nevi and surrounding skin.

Bscan image segmentation by thresholding using cooccurrence matrix analysis

In this paper we present innovative research for the processing of an uktrasonic image obtained in non-destructive testing (NDT). We also present an algorithm for automatic thresholding segmentation. The image analysis is based on the calculation of cooccurrence matrices. The method we used is the Spatial Grey-Level Dependence Method (SGLDM); it shows the distribution of the intensities of pairs of pixels separated by a vector d. We show that it is necessary to build optimized matrices in order to improve the existing techniques while adapting to the specifics of the ultrasonic image obtained in NDT. We solve for the vector d through an automatic analysis of the matrix behaviour for several vectors based on the characterization curves. The developed method allows us to present an efficient thresholding algorithm that is tested on real images.

近接圃場画像に対するテクスチャ解析手法の有効性 牧草混生群落の草種割合推定を例として

The purpose of the present study was to evaluate the applicability of three methods of texture analysis (Spatial Gray Level Dependence Method: SGLDM, Gray Level Run Length Method: GLRLM and Power Spectrum Method: PSM) to photographic images from close distance. These methods have been applied primarily to satellite images. For an example, a 1m×1m ground surface covered by a mixture of orchard grass and white clover was photographed, and the photographic image was divided into 49 square blocks. The composition of the 2 species was estimated for each block.The image of the mixed pasture was characterized by the mixed texture of the 2 species with very different leaf shapes and by the heterogeneous texture due to the roughness of the canopy surface.Most of the textural features related fairly well to the composition of the 2 species for all the three different methods. They, however, showed a significant difference in stability and linearity.In the estimation of a dominant species, classification accuracy exceeded 80% in all the methods. In a comparison between the observation and estimation of the species composition, the coefficient of correlation exceeded 0.8. In particular, GLRLM showed the highest value of 0.918 among the three methods. Standard error of estimation was 11.6% in GLRLM, 16.0% in SGLDM and 16.7% in PSM.

Statistical feature matrix for texture analysis

A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major properties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis.

Sum and Difference Histograms for Texture Classification

The sum and difference of two random variables with same variances are decorrelated and define the principal axes of their associated joint probability function. Therefore, sum and difference histograms are introduced as an alternative to the usual co-occurrence matrices used for texture analysis. Two maximum likelihood texture classifiers are presented depending on the type of object used for texture characterization (sum and difference histograms or some associated global measures). Experimental results indicate that sum and difference histograms used conjointly are nearly as powerful as cooccurrence matrices for texture discrimination. The advantage of the proposed texture analysis method over the conventional spatial gray level dependence method is the decrease in computation time and memory storage.

A Theoretical Comparison of Texture Algorithms

An evaluation of the ability of four texture analysis algorithms to perform automatic texture discrimination will be described. The algorithms which will be examined are the spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), the gray level difference method (GLDM), and the power spectral method (PSM). The evaluation procedure employed does not depend on the set of features used with each algorithm or the pattern recognition scheme. Rather, what is examined is the amount of texturecontext information contained in the spatial gray level dependence matrices, the gray level run length matrices, the gray level difference density functions, and the power spectrum. The comparison will be performed in two steps. First, only Markov generated textures will be considered. The Markov textures employed are similar to the ones used by perceptual psychologist B. Julesz in his investigations of human texture perception. These Markov textures provide a convenient mechanism for generating certain example texture pairs which are important in the analysis process. In the second part of the analysis the results obtained by considering only Markov textures will be extended to all textures which can be represented by translation stationary random fields of order two. This generalization clearly includes a much broader class of textures than Markovian ones. The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.

Toward a structural textural analyzer based on statistical methods

This paper reports investigations aimed at developing a feature set for the Spatial Gray Level Dependence Method (SGLDM) which measures visually perceivable qualities of textures. In particular it will be shown that the inertia measure commonly used with the SGLDM can be used to characterize the placement rules and the unit pattern of periodic textures. In this way one may formulate a structural approach to texture analysis based on the statistical SGLDM. To mathematically verify that the features used with the SGLDM can be used to characterize the unit pattern and placement rules of a periodic texture, a mathematical tiling theory model is proposed. This model allows one to develop the mathematical machinery necessary to prove the result. In a companion paper other features which measure visually perceivable qualities of patterns will be developed. The reason for concentrating on the SGLDM for developing such a feature set is predicated on perceptual psychology experiments and comparison studies of various texture algorithms. All of these studies indicate that second-order probabilities of the type measured by the spatial gray level dependence matrices are important in human texture discrimination and that these matrices contain more important texture-context information than the intermediate matrices of other statistical texture analysis algorithms.