Gray Level Difference Statistics Research Articles

Detection of Cerebrovascular Diseases using Novel Discrete Component Wavelet Cosine Transform.

Detecting and classifying a brain tumor amid a sole image can be problematic for doctors, although improvements can be made with medical image fusions. A brain tumor develops in the tissues surrounding the brain or the skull and has a major impact on human life. Primary tumors begin within the brain, whereas secondary tumors, identified as brain metastasis tumors, are generated outside the brain. This paper proposes hybrid fusion techniques to fuse multi-modal images. The evaluations are based on performance metrics, and the results are compared with conventional ones. In this paper, pre-processing is done considering enhancement methods like Binarization, Contrast Stretching, Median Filter, & Contrast Limited Adaptive Histogram Equalization (CLAHE). Authors have proposed three techniques, PCA-DWT, DCT-PCA, and Discrete ComponentWaveletCosine Transform (DCWCT), which were used to fuse CT-MR images of brain tumors. The different features were evaluated from the fused images, which were classified using various machine learning approaches. Maximum accuracy of 97.9% and 93.5% is obtained using DCWCT for Support Vector Machine (SVM) and k Nearest Neighbor (kNN), respectively, considering the combination of both feature's shape & Grey Level Difference Statistics. The model is validated using another online dataset. It has been observed that the classification accuracy for detecting cerebrovascular disease is better after employing the proposed image fusion technique.

(Retracted) Feature extraction method CNDFA for target contour of coal and gangue based on multifractal

Feature extraction is an important factor to improve the recognition rate of coal and gangue. The existing feature extraction methods have some shortcomings in coal and gangue recognition, such as unsatisfactory recognition rate for actual images. Therefore, aiming at the shortcomings of existing coal and gangue feature extraction methods in coal and gangue recognition, the contour feature extraction method is researched based on images analysis to coal and gangue recognition. The center normalization detrended fluctuation analysis (CNDFA) feature extraction algorithm of contour is proposed based on the process of contour feature extraction for coal and gangue. Based on the analysis of the representative features of coal and gangue, the extraction process of target contour features is established based on hardness difference. Combined with the overall features of contour curve after detrending, a center normalized CNDFA feature extraction algorithm is proposed. First, the detrended analysis of contour curve is realized by least square optimal fitting, and then the detrended data are normalized. Finally, the contour features are described quantitatively by multifractal (MF) spectrum to form the geometric features of the target contour curve, which is used to train the support vector machine classifier. The experiment is carried out on the basis of image preprocessing, and the CNDFA method and other feature extraction methods, such as wavelet, gray level co-occurrence matrix, gray level difference statistics, auto-correlation function, and MF, are applied to the contour feature extraction of coal and gangue. Through the comprehensive comparison of the results after different methods recognition in confusion matrix, accuracy, and coal cleanliness, it is concluded that the overall effect of the CNDFA method is better than other methods, and the accuracy is improved by 5% to 25%. The results show that the CNDFA method has better performance. Compared with the other methods, it can better extract the contour features to improve the recognition rate of coal and gangue.

Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

Autotransformer (AT) is the most core power supply equipment, and overvoltage and short circuit (SC) fault may lead to winding deformation, which will have a negative impact on its insulation and even affect the operation of a train. The frequency response analysis (FRA) is widely used for detecting winding faults in a transformer. However, the direct measure of FRA for each split winding fails because the split windings are adopted to satisfy the impedance requirement of a high-speed railway, where the windings are connected inside the tank. A novel fault interpretation method based on image features and binary tree support vector machine (SVM) is proposed, which can get the condition of three windings in one measurement. Winding faults caused by different windings are simulated, including SC defect, axial deformation, and series capacitance variation, and the FRA curves are measured under various faults. Then, the features of the gray-level gradient co-occurrence matrix and the gray-level difference statistics are got from the polar plot of FRA. Finally, the image features are used as the inputs to the binary tree SVM for fault type and faulty winding classification. The results show that the proposed method has high accuracy for identifying fault type and faulty winding in AT.

Carotid artery ultrasound texture, cardiovascular risk factors, and subclinical arterial disease: the Multi-Ethnic Study of Atherosclerosis (MESA).

This pilot study determined if the ultrasound texture feature "contrast" was associated with cardiovascular disease (CVD) risk factors and subclinical arterial disease. We evaluated ultrasound images of the right common carotid artery (CCA) from a convenience sample of 151 participants and examined relationships between contrast, CVD risk factors, carotid intima-media thickness (IMT) and coronary artery calcium (CAC). Grey level difference statistics algorithms were used to evaluate the texture feature "contrast" from carotid ultrasound images. Right CCA IMT measurements were made in triplicate in the distal 1 cm segment of the far wall of the artery and CAC score was measured using the Agatston scoring method. In individual models that included age, sex and race, grey level difference statistics contrast (outcome) was associated independently with age [beta (standard error) = -0.87 (0.38) per year; p = 0.02], C-reactive protein [-2.22 (0.96) per mg dl-1; p = 0.02], high-density lipoprotein cholesterol [0.61 (0.24) per mg dl-1; p = 0.01] and CCA IMT [-0.06 (0.02) microns; p = 0.001]. Other CVD risk factors and CAC were not associated independently with contrast. These findings support the potential use of the ultrasound texture contrast for evaluating arterial injury and CVD risk. Advances in knowledge: This paper contributes to the literature in that it describes how the greyscale texture feature "contrast" is related to CVD risk factors.

Abstract 598: Carotid Artery Ultrasound Texture, Cardiovascular Risk Factors, and Subclinical Arterial Disease: The Multi-Ethnic Study of Atherosclerosis (Mesa)

Background: Ultrasound texture “contrast” can be used to characterize the arterial wall non-invasively. Texture contrast provides information about the distribution and gray level differences of the pixels in the arterial wall and may describe early changes related to arterial injury. We determined if ultrasound texture contrast was associated with cardiovascular disease (CVD) risk factors and subclinical arterial disease. Methods: We evaluated ultrasound images of the distal right common carotid artery from a convenience sample of 151 participants from the first examination of the Multi-Ethnic Study of Atherosclerosis, a population-based cohort of individuals without clinical CVD. Images were digitized, normalized, and standardized to a pixel density of 20/mm. Plaque texture analysis software (LifeQ Medical, Cyprus) used the gray level difference statistics method to determine the contrast of the far wall of the carotid intima-media complex. Multivariable linear regression models (adjusted for age, sex and race/ethnicity) were used to examine relationships between contrast, CVD risk factors (age, BMI, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol, triglycerides, hypertension, diabetes mellitus, smoking, glomerular filtration rate, C-reactive protein, interleukin-6, D-dimer, fibrinogen, alcohol consumption, education level, physical activity level, statin use), carotid intima-media thickness (IMT), coronary artery calcium (CAC), and CVD risk score. Results: The 151 participants were mean (standard deviation) 68.1 (9.2) years old (54% female; 31% Hispanic, 28% Black, 10% Chinese, and 31% White). In models that included age, sex, and race, contrast was associated independently with age (beta [standard error] -0.9 [0.4] per year; p=0.02), HDL-C (0.6 [0.2] per mg/dL; p=0.02), C-reactive protein (-2.3 [0.9] per mg/L; p=0.02), and carotid IMT (-1.3 [0.4] microns; p=0.001). Other CVD risk factors and CAC were not associated independently with contrast. Conclusions: Lower contrast, an ultrasound texture feature, is associated with increasing age, lower HDL-C, higher CRP, and higher carotid IMT, supporting its potential use for evaluating arterial injury and CVD risk.

Texture based classification of the severity of mitral regurgitation

Clinically, the severity of valvular regurgitation is assessed by manual tracing of the regurgitant jet in the respective chambers. This work presents a computer-aided diagnostic (CAD) system for the assessment of the severity of mitral regurgitation (MR) based on image processing that does not require the intervention of the radiologist or clinician. Eight different texture feature sets from the regurgitant area (selected through an arbitrary criterion) have been used in the present approach. First order statistics have been used initially, however, observing their limitations, the other texture features such as spatial gray level difference matrix, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws’ textures energy measure, fractal dimension texture analysis and Fourier power spectrum have additionally been used. For the classification task a supervised classifier i.e., support vector machine has been used in the present approach. The classification accuracy has been improved significantly by using these texture features in combination, in comparison to when fed individually as input to the classifier. The classification accuracy of 95.65±1.09, 95.65±1.09 and 95.36±1.13 has been obtained in apical two chamber, apical four chamber and parasternal long axis views, respectively. Therefore, the results of this paper indicate that the proposed CAD system may effectively assist the radiologists in establishing (confirming) the MR stages, namely, mild, moderate and severe.

Computer-aided diagnosis in hysteroscopic imaging.

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

Difference theoretic feature set for scale‐, illumination‐ and rotation‐invariant texture classification

Texture identification and classification under varying scale, rotation and illumination conditions is a challenging task in pattern recognition and grey level difference statistics have been extensively used for this purpose. This study presents a new set of features for scale-, rotation- and illumination-invariant texture classification derived from the correlated distributions of local and global grey level differences of intensities in the texture image. The authors analyse the terms in the correlation formula for determining the difference-based feature set that is invariant and unique for a texture class. A comprehensive evaluation is performed on a huge database of digitally created texture samples of varying scale, orientation and brightness. The one-nearest neighbour classifier is used in the authors' experiments and the results indicate high classification accuracy for the proposed feature vector under varying scale, rotation and brightness conditions. The proposed method is compared with the highly efficient rotation- and illumination-invariant local binary pattern (LBP) and LBP variance techniques and the scale- and rotation-invariant MRS4 technique and is found superior in performance with an additional advantage of reduced feature dimension.

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.

An information fusion based method for liver classification using texture analysis of ultrasound images

This paper presents a method for classification of liver ultrasound images based on texture analysis. The proposed method uses a set of seven texture features having high discriminative power which can be used by radiologists to classify the liver. Feature extraction is carried out using the following texture models: Spatial Gray Level Co-occurrence Matrix, Gray Level Difference Statistics, First order Statistics, Fourier Power Spectrum, Statistical Feature Matrix, Law’s Texture Energy Measures and Fractal Features. Based upon the results of Linear Discriminative Analysis (LDA) followed by box-plot analysis and Pearson’s correlation coefficient, 7 best features from a set of 35 features are selected. These selected features are then fused using a linear classifier. The novelty of the proposed method is that, it combines the best features from different texture domains along with their weights and ‘weighted z-score’ values. Subsequently, these values are used to compute a discriminative index for liver classification. The results show that this method has overall classification accuracy of 95% and low computational complexity.

2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

BackgroundIn the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction.MethodsWe investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences.ResultsFor the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium.ConclusionThis study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

Combined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization

Combined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization

A Modular Software System to Assist Interpretation of Medical Images—Application to Vascular Ultrasound Images

Improvements in medical imaging technology have greatly contributed to early disease detection and diagnosis. However, the accuracy of an examination depends on both the quality of the images and the ability of the physician to interpret those images. Use of output from computerized analysis of an image may facilitate the diagnostic tasks and, potentially improve the overall interpretation of images and the subsequent patient care. In this paper, Analysis, a modular software system designed to assist interpretation of medical images, is described in detail. Analysis allows texture and motion estimation of selected regions of interest (ROIs). Texture features can be estimated using first-order statistics, second-order statistics, Laws' texture energy, neighborhood gray-tone difference matrix, gray level difference statistics, and the fractal dimension. Motion can be estimated from temporal image sequences using block matching or optical flow. Image preprocessing, manual and automatic definition of ROIs, and dimensionality reduction and clustering using fuzzy c-means, are also possible within Analysis. An important feature of Analysis is the possibility for online telecollaboration between health care professionals under a secure framework. To demonstrate the applicability and usefulness of the system in clinical practice, Analysis was applied to B-mode ultrasound images of the carotid artery. Diagnostic tasks included automatic segmentation of the arterial wall in transverse sections, selection of wall and plaque ROIs in longitudinal sections, estimation of texture features in different image areas, motion analysis of tissue ROIs, and clustering of the extracted features. It is concluded that Analysis can provide a useful platform for computerized analysis of medical images and support of diagnosis

Multifeature texture analysis for the classification of clouds in satellite imagery

The aim of this work was to develop a system based on multifeature texture analysis and modular neural networks that will facilitate the automated interpretation of satellite cloud images. Such a system will provide a standardized and efficient way for classifying cloud types that can be used as an operational tool in weather analysis. A series of 98 infrared satellite images from the geostationary satellite METEOSAT7 were employed, and 366 cloud segments were labeled into six cloud types after combined agreed observations from ground and satellite. From the segmented cloud images, nine different texture feature sets (a total of 55 features) were extracted, using the following algorithms: statistical features, spatial gray-level dependence matrices, gray-level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws' texture energy measures, fractals, and Fourier power spectrum. The neural network self-organizing feature map (SOFM) classifier and the statistical K-nearest neighbor (KNN) classifier were used for the classification of the cloud images. Furthermore, the classification results of the nine different feature sets were combined, improving the classification yield for the six classes, for the SOFM classifier to 61% and for the KNN classifier to 64%.

Fuzzy neural network-based texture analysis of ultrasonic images.

The efficacy of a novel fuzzy neural network classifier for the characterization of ultrasonic liver images based on texture analysis techniques is investigated. Classification features are extracted with the use of image texture analysis techniques such as fractal dimension texture analysis, spatial gray-level dependence matrices, gray-level difference statistics, gray-level run-length statistics, and first-order gray-level parameters. These features are fed to a neural network classifier based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed, leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed.

A Bayesian Cloud Mask for Sea Surface Temperature Retrieval

Abstract Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed. Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improv...

Texture features for classification of ultrasonic liver images

The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images.

Extraction of surface orientation from texture using the gray level difference statistics

The problem of extracting orientation of an object surface from a monocular image is one of the important tasks in computer vision. Most of the existing methods for extracting surface orientation are ones using the structural features of texture such as texel and edge. However, to represent texture features statistically is shown to be effective also in texture discrimination and segmentation. Thus, in this paper we propose a method for extracting surface orientation using the statistical feature of a texture image. First, we assume uniformity of a probability density function of difference statistic on object surface; then using the fact that the difference statistics depend on the geometric factor of length and orientation, we formulate the relationship between distortion of a density function in an image caused by perspective projection and the object surface. Then we derive an algorithm for finding the object surface orientation by search based on this formulation. In addition we apply this method to simulation images and real images to show its effectiveness. This enables us to extract object surface directly from a gray level image without extracting the texel or edge (whose extraction is required in the existing methods).

Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data

This investigation has attempted to discover appropriate texture descriptors and to reveal more clearly the importance of texture analysis techniques for multispectral cloud classification. The textural features considered in this study include both spatial and frequency features. The spatial features were mainly those based on spatial grey-level difference statistics and circular Moran autocorrelation measures. The frequency features were those based on summed energies of polar co-ordinate Fourier power spectra and entropy-based measures of the spatial distribution of frequency entries in the polar spectra. Some other textural features, such as the Roberts gradient measure, were also investigated. The work was performed with TIROS-N AVHRR image data acquired in the late spring of 1979 over areas near the British Isles. The results of the evaluation and corresponding conclusions show which individual, and group of textural features, appear the most appropriate for aiding multispectral cloud classification.