Texture classification of gray-level images by multiscale cross co-occurrence matrices

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

Texture analysis as an approach to tissue characterization based on the two-dimensional spatial distribution of ultrasound amplitude or grey-level pixels within a region of interest (ROI) is well-established and have been used extensively in Radiology and echocardiography. Objective: We performed a quantitative statistical analysis of various texture parameters of EUS images in patients with histologically confirmed pancreatic adenocarcinoma to evaluate if such statistical techniques can reliably distinguish between malignant and normal pancreatic tissue. Methods: Linear endosonographic images of 22 patients who presented with pancreatic mass and underwent EUS guided FNA for establishing diagnosis of pancreatic adenocarcinoma were analyzed with an image analyzing software (ImageJ, NIH). Multiple ROIs were digitally selected both in the area of the malignant mass and normal pancreatic tissue by an experienced endosonographer. Texture analysis was performed using: histogram (first order statistics) run-length and co-occurrence matrix (measuring features of runs of pixels and distribution of pairs of pixels), gradient analysis (spatial distribution of pixels), auto-regressive analysis (measurement of local interaction amongst pixels of different grey level values) and also wavelet analysis (which measures parameters of spatial frequency). A neural network (NN) based predictive model was built, trained and validated using the extracted texture features for classification of malignant tissue from normal. Methods: A total of 70 ROIs representing malignant tissue and 110 representing normal pancreatic tissue was available for analysis and for each ROI, a total 256 statistical parameters were initially extracted and subsequently 10 best discriminating (based on highest Fisher's co-efficient) features were retained. Overall, 3 types of features had high discriminatory power: Kurtosis (which is a measure of the steepness of the pixel distribution), angular second momentum and horizontal and vertical grey level non-uniformity (parameters estimating the second order joint conditional probability density functions of spatial distribution of two neighboring pixels). Using a random selection of the half of the data set (90 ROIs), a multi-layered perceptron NN model was built, trained using the 10 features as input variables for prediction of malignant tissue and then the model was validated on the rest of the 90 ROIs. The NN model had a very high classification performance with an area under Receiver Operating Characteristic Curve of 0.91 (PPV of 80% and NPV 90%). Conclusion: Digital image analysis of texture features on EUS images is highly accurate in differentiating malignant from normal pancreatic tissue with performance characteristics similar to aspiration cytology.

Purpose: Endoscopic digital images contain rich information on texture and color. There is increasing interest in differentiating tissue pathology based on the patterns of color and texture features of endoscopic images extracted by digital image analysis (DIA). Objective: In this study, we applied techniques of DIA to endoscopic images of diminutive polyps in order to identify the color and texture features of endoscopic images that may differentiate adenomatous polyps from hyperplastic polyps. Methods: Methods: Our endoscopic image database was searched to retrieve and store digital images of diminutive polyps with diagnosis confirmed by histopathology. Texture analysis was performed on multiple regions of interest (ROIs) digitally selected in these images using the MatLab image processing toolbox; the following texture parameters were extracted: histogram (first order statistics) run-length and co-occurrence matrix (measuring features of runs of pixels and distribution of pairs of pixels), gradient analysis (spatial distribution of pixels), auto-regressive analysis (measurement of local interaction amongst pixels of different grey level values) and also wavelet analysis (which measures parameters of spatial frequency). Principal component analysis (PCA) was used for data reduction, and an artificial neural network (NN) based predictive model was built, trained and validated using the extracted texture features for classification of adenomatous polyps from hyperplastic polyps. Results: Results: A total of 110 and 109 ROIs were selected from endoscopic images of 30 and 28 patients with diminutive adenomatous and hyperplastic polyps, respectively. The sensitivity and specificity of the multilayered perceptron neural network classification model were both 82% with an area under the receiver operating characteristic (ROC) curve of 0.87. Three types of features of pixel distribution had high discriminatory power: Kurtosis (measure of the steepness of the pixel distribution), angular second momentum and horizontal and vertical grey level non-uniformity (parameters estimating the second order joint conditional probability density functions of spatial distribution of two neighboring pixels) Conclusion: Conclusion: Results of this exploratory study suggest that the technique DIA may be a clinically useful adjunct for non-invasive automated diagnosis of diminutive colon polyps, particularly if available as a real-time application.

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

A newly emerged technique known as Digital Rock Physics demonstrates an ability to characterize properties of the porous media. This technique is based on the imaging of rock micro-structure using a micro-CT scanner. Images are segmented based on their grayscale values to extract pore network from the solid phase. Then, rock properties are estimated using extracted pore network and numerical simulations. Porosity and absolute permeability are two essential properties that can be derived from grayscale images. These properties represent storage and flow capacity of the rock. Some rock samples, particularly carbonate rocks have complex micro-structures at several length scales. Due to limited image resolution, 3D images of carbonate rock may not have top-bottom pore connectivity. In this case, one unable to simulate fluid flow throughout the images. Therefore, permeability is computed on small image sub-volumes, where pore connectivity is revealed locally. Such approach requires long simulation runs. In this paper, a new approach is proposed to estimate permeability from 3D carbonate rock images, where pore connectivity is not revealed from top to bottom. In this approach, first a number of texture classes that represents various textures in the 3D image are identified. For each identified texture class, several sub-volumes from the 3D image are extracted. These sub-volumes are representative of the identified textures and have local pore connectivity. To simulate fluid flow through the pore network of extracted sub-volumes Lattice Boltzmann method (LBM) is used. Permeability is then calculated using Darcy's equation. After determining permeability ranges of each textural class, the 3D image is divided into sub-volumes. Then, each sub-volume is classified into one of the specified texture classes using a modified version of the texture classification algorithm, Local Binary Pattern (LBP). In this study, traditional 2D LBP texture feature vector is adopted to handle 3D volume classification. After classification, each sub-volume is assigned a permeability value that is equivalent to its texture class. Finally, the permeability of the whole 3D image is computed by simulating Darcy's flow through the all sub-volumes. Preliminary outcomes of the proposed technique indicate that it is able to closely estimate experimental results from the laboratory.

A clustering method based on multidimensional texture analysis

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these-rather elementary-difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptors by Lazebnik et al.

Objective: to assess the classification capability dealing with CT images, by means of Decision Tree classifier. Methods: the CT images were provided by the hospital was used as the data source. In this study.normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid CT images were selected.each 200 pieces. Then the texture features were extracted by gray gradient co-occurrence matrix(GGCM) and gray scale histogram. At last. Decision tree classifier were used for classification, which aimed to verify Which feature is more suitable for decision tree classification, and Parameter estimation is used to evaluate the classifier model. Results: For normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid, the classification accuracy of gray level co-occurrence matrix was 71%,69% and 69%, respectively. the classification accuracy of gray scale histogram was 74%,63.5% and 69%.the classification accuracy of comprehensive features was 75%, 70.5% and 80.5%. Conclusion: The classification accuracy of comprehensive feature is 11.5% higher than that of single features, which are more suitable for the classification of polycystic hepatic hydatid images. This algorithm can provide reference for the classification of CT images.

Detection of brain tumour is very important current scenario of the health care society. Image processing techniques are used to extract the abnormal tumour portion and other features in the brain. Brain tumor is an abnormal mass of lesson in which cells grow up and multiply uncontrollably, apparently unregulated by the mechanisms that control cells. Several techniques like Segmentation, morphological have been developed for detection of tumor in the brain. Texture is a critical feature of several image types and textural features have a lot of application in image processing, content-based image retrieval and so on. There are several ways of extracting these features and the most common way is by using a gray-level co-occurrence matrix (GLCM). In our proposed work Texture characterisation has been made to obtain the Haralick features and SVM classifier is used in the Texture classification algorithm which used in detecting the brain tumor. This technique has been tested for 45 images, true positives are 33, True negative is 1, false positive is 1, and True negatives are 10. Sensitivity 97.0%, Specificity 90.9%, Precision or Positive Predictive Value (PPV) 97.0%,Negative Predictive Value (NPV)90.9%, Accuracy 95.0%.

Several spatial features are compared for the spatial/spectral classification of hyperspectral data. These features are extracted from texture spectra, co-occurrence matrices and morphological profiles. First, a PCA (Principal Components Analysis) is carried out on the hyperspectral image and textural features are computed on the first principal components. These textural features are concatenated together with spectral features (the principal components previously used) and the resulting image vector is then classified using SVM (Support Vector Machines) and a gaussian mixture algorithm. In the latter case, a hierarchical classification is used as a post-processing in order to reach a desired number of classes.

From the texture analysis of image content, we propose a steganalytic method to detect spatial domain steganography in grayscale images. First of all, based on the local linear vectors, which are selected carefully and sensitive to image texture, images are decomposed into several textural detail subbands by the local linear transform (LLT). Then the statistical distribution of the LLT coefficient is modeled by using the generalized Gaussian distribution. Finally, novel textural features of the LLT coefficient histogram and cooccurrence matrix are extracted for steganalyzers implemented by the support vector machine. Extensive experiments are performed on four diverse uncompressed image databases and seven typical spatial domain steganographic algorithms, such as the highly undetectable stego. The results reveal that the proposed scheme is universal for detecting spatial domain steganography. By comparison with other well-known feature sets, our presented feature set offers the best performance under most circumstances.

Image classification is one among the significant tasks that can benefit industry through advancement in science and technology. Texture images possess a specific pattern that can be utilized to uniquely identify a texture class. Classification of Texture images can be done through proper selection of parameter that uniquely identifies an image in a group containing images of different classes. Machine learning approaches are currently the choice of implementation to provide accuracy and robustness in various fields like automatic image recognition, registration, and analysis. This work addresses machine learning methods to classify learnt texture samples through feature fusion of parameters obtained through wavelet and texture analysis.

Prediction of Malignant Cell Infiltration Patterns with Texture Features of Biopsy-Correlated Positron Emission Tomography of Osteolytic Lesions in Multiple Myeloma

In this paper, a new method for supervised classification of terrain types in polarimetric Synthetic Aperture Radar (Pol-SAR) images is proposed. This technique is a combination of the texture classification and the maximum likelihood classification based on the complex Wishart distribution for the polarimetric covariance matrix. The texture features are first extracted from the span image based on co-occurrence matrices; and then the classifier combines the texture features with the distance measure based on polarimetric information to obtain the results. Using a NASA/JPL AIRSAR image, the effectiveness of the proposed method is demonstrated.