Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.

Integrated ultrasonic backscatter from normal myocardium has been shown to vary with the phase of cardiac contraction (decreasing from end-diastole to end-systole) in previous studies of open-chest dogs. If confirmed, this finding would have important implications for clinical application of ultrasonic tissue characterization. Our hypothesis was that a cardiac cycle-dependent variation in regional average gray level would be detected on analysis of digitized two-dimensional echocardiograms. We analyzed echocardiographic images from 16 subjects in whom normal, technically good studies were obtained with a commercial phased-array scanner and a 2.25 MHz transducer. Images from six subjects were digitized from stop-frame photographs and those from 10 subjects were obtained directly in digital format from the scanner. Average gray level was measured in a portion of the left ventricular posterior wall in parasternal long-axis images obtained at end-diastole and end-systole by both photographic and digital-image acquisition. In seven of the subjects from whom digital images were acquired, left ventricular posterior wall gray level and ventricular septal gray level were also evaluated on parasternal short-axis images. In images digitized by the photographic technique, mean posterior wall gray level decreased significantly from end-diastole (175 +/- 5.8 SEM) to end-systole (167 +/- 5.1, p less than .05). Similarly, in images digitized directly, mean posterior wall gray level in the long-axis view decreased from end-diastole (71 +/- 3.4) to end-systole (59 +/- 2.5, p less than .005).(ABSTRACT TRUNCATED AT 250 WORDS)

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.

In marketing channels, bread is sometimes delivered in a frozen sate for distribution. Changes occur in physical dimensions, crumb grain and appearance of slices. Ten loaves, twelve bread slices per loaf were scanned for digital image analysis and then frozen in a commercial refrigerator. The bread slices were stored for four weeks scanned again, permitted to thaw and scanned a third time. Image features were extracted, to determine shape, size and image texture of the slices. Different thresholds of grey levels were set to detect changes that occurred in crumb, images were binarized at these settings. The number of pixels falling into these gray level settings were determined for each slice. Image texture features of subimages of each slice were calculated to quantify slice crumb grain. The image features of the slice size showed shrinking of bread slices, as a results of freezing and storage, although shape of slices did not change markedly. Visible crumb texture changes occurred and these changes were depicted by changes in image texture features. Image texture features showed that slice crumb changed differently at the center of a slice compared to a peripheral area close to the crust. Image texture and slice features were sufficient for discrimination of slices before and after freezing and after thawing.

Nowadays, the hard correction is a familiar method for image seam-line removing. But the method needs accurate gray difference around the seam-line which is difficult to achieve occasionally. To overcome such drawbacks, a novel seam-line removing method was proposed in the paper. First, optimal texture matching blocks beside the seam-line are searched, and the average gray level of the two blocks can be obtained. Then, the average gray level of the two blocks is adjusted to estimate the gray level of the same target in different illumination environment. Finally, the illumination is corrected based on the average gray level to remove the seam-line. The theoretical analysis and experiment results show that the proposed method is more effectual then traditional methods.

The aims were: (1) to evaluate whether differences in absolute and cyclic echocardiographic image amplitude exist in different layers (subendocardium and subepicardium) and regions (septal, anterior, lateral, inferior wall) of the canine left ventricle; (2) to assess the dependence of these variables upon local variations of coronary blood flow. In six anaesthetised open chest dogs the circumflex coronary artery was cannulated and perfused by a roller pump with blood from their own femoral artery. Maximum coronary vasodilatation was obtained by continuous adenosine infusion. The absolute values and the transmural distribution of coronary blood flow were measured by radionuclide labelled microspheres. Echo images were obtained in short axis view by a commercially available electronic sector scanner with a 5.0 MHz transducer directly placed on the epicardial surface of the right ventricle, and digitised off-line into a matrix of 256 x 256 pixels with 25% grey level per pixel. The average grey level was calculated for each region of interest. In 32 different conditions, circumflex flow ranged from 0.80 to 12.89 ml.min-1.g-1 and the endocardial/epicardial ratio of flow from 0.53 to 1.73. In the circumflex region (subjected to flow changes) segmental amplitude varied from 76(SD 20) (end diastole) to 56(18) (end systole), p < 0.001. In all regions, a consistent cyclic variation was found, ranging from 9(14) to 28(16)%. For all levels of flow, subendocardial and subepicardial regions showed similar values of both absolute amplitude and cyclic variation. No significant relationship was found between transmural distribution of blood flow and either segmental amplitude (r = 0.26) or cyclic variation (r = 0.04). (1) a consistent cyclic grey level variation is present in all regions of the canine left ventricle, but subendocardial and subepicardial layers show similar values of both absolute amplitude and cyclic variation; (2) in the absence of severe underperfusion and echocardiographically detectable dyssynergy, absolute amplitude and cyclic variation are totally unrelated to changes in coronary blood flow.

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.

Homogram, or histogram based on homogeneity is employed in our algorithm. Histogram thresholding is a classical and efficient method for the segmentation of various images, especially of CT images. However, MR images are difficultly segmented via this method; as the gray levels of their pixels are too similar to distinguish. The regular histogram of a MR image is usually plain, thus the peaks and valleys of the histogram are hard to find and locate precisely. We proposed a new definition of homogeneity for which a series of sub-images are employed to compute. Therefore, both local and global information are taken in accounted. Then the image is updated with the homogeneity weighted original and average gray levels. The more homogeneous the pixel is, the closer the updated gray level is to the average. The new histogram is calculated based on the updated image. It is much steeper than the regular one. Some indiscernible peaks in the regular histogram can be recognized easily from the new histogram. Therefore a simple but agile peak-finding approach is able to determine objects to segment and corresponding thresholds exactly. Segmentation via thresholding is feasible now even in MR images. Moreover, our algorithm remains speedy even though the accuracy of segmentation advances.

To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

Canopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative. To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables. The analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R2 = 0.91076, RMSE = 0.00070; validation set; R2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%. This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.

Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly.

PURPOSEMost factors identified in prior studies on meningioma enlargement are qualitative, with limited quantitative evaluation. Predicting future tumor growth from initial imaging of incidentally discovered meningiomas could inform treatment decisions. This study quantitatively evaluates imaging data at diagnosis to identify characteristics associated with tumor enlargement. METHODSWe compared MRI T2 features from the initial imaging of 10 patients in a clearly enlarged group with 10 in a non-enlarged group. Using 3D Slicer and Slicer Radiomics plug-ins, we created a program to set regions of interest (ROIs) in the tumor and calculate various features. Additional ROIs were placed in the outer tumor edge, brain, and white matter contralateral to the tumor. Inter-ROI ratios of features were calculated and compared using PyRadiomics software. We analyzed 107 features across seven classifications: one-dimensional statistics, shape features, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighbouring Gray Tone Difference Matrix. A t-test was conducted to identify significant predictors. RESULTSTwenty-four features showed significant differences (p<0.05), including 10 one-dimensional statistics, 1 shape feature, 5 Gray Level Co-occurrence Matrix, 6 Gray Level Run Length Matrix, 1 Gray Level Size Zone Matrix, and 1 Gray Level Dependence Matrix. CONCLUSIONSWe identified potential quantitative predictors of incidentally discovered meningioma growth. Future analysis with a larger sample size will refine these predictors and support the development of a predictive formula and corresponding program.

Classifying different types of sea ice plays a significant role in marine navigation, especially on the Northwest Passage in the Arctic. In this paper, a U-Net model is trained to identify multi-year ice (MYI), first-year ice (FYI), and open water (OW) from synthetic aperture radar (SAR) images. 14 Sentinel-1 A/B Extended Wide (EW) mode images, acquired in the Beaufort Sea and Svalbard between January and March 2020, and their gray level co-occurrence matrix (GLCM) texture feature are split into chips to be fed into the model for training and testing. The GLCM is a set of images reflecting the comprehensive information of direction, adjacent interval, and variation amplitude of the gray level of original image. It can also be understood as texture features of original image. It is the basis of analyzing local patterns in an image and their arrangement rules. Dissimilarity of HH (HH-Dis) chosen from 20 kinds of available GLCM texture features is combined with horizontal-vertical (HV) polarization, horizontal-horizontal (HH) polarization, and incident angle as inputs. The LabelMe software creates labels with visual interpretation. The proportions of FYI and MYI data in training data were adjusted to approximately equal, to avoid bias of model prediction results towards dominant categories. The result shows that the combination of HH, HV, incident angle and HH-Dis has the highest precision of 92.10%, although the precision is 89.35% when only HH and HV information combined. This result reflects the potential of adding GLCM for sea ice classification.

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.