Gray-level Co-occurrence Matrix Texture Analysis Research Articles

A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images.

The effects of 20-hydroxyecdysone on nuclear shape, heterochromatin quantity and gray-level co-occurrence matrix texture analysis of adrenal zona fasciculata cells in an obese gerbil (Gerbillus tarabuli) model for metabolic syndrome: a correlational study.

The aim of this study was to reveal the effects of obesity and phytotherapy with 20-hydroxyecdysone (20E) on the nuclei of adrenal zona fasciculata (ZF) in the gerbil Gerbillus tarabuli by analyzing nuclear shape and gray-level co-occurrence matrix (GLCM) texture characteristics and by quantifying heterochromatin. Twelve gerbils were divided into three groups: control (C), HC and HC-20E (animals receiving a high-calorie-diet without or with a supplement of 20E, respectively). The adrenals were removed and fixed for histological and statistical analysis. Principal component analysis showed a positive correlation of area, perimeter and textural correlation in C. Nevertheless, a negative correlation was recorded for contrast and entropy. The obesity caused a disorder in nuclear texture; negative correlation was noted with heterochromatin fraction, which may be related to increased ZF activity. However, administration of 20E seems to improve the nuclear state by preserving circularity, uniformity and homogeneity of nuclei as well as the proportion of heterochromatin, which could be a sign of a downregulation of cell activity.Our results suggest that new techniques of image processing could contribute to the understanding of nuclear changes associated with obesity and its possible therapy in this gerbil model for metabolic syndrome.

Differentiation of Amyloid Plaques Between Alzheimer's Disease and Non-Alzheimer's Disease Individuals Based on Gray-Level Co-occurrence Matrix Texture Analysis.

Amyloid plaques, one of the main hallmarks of Alzheimer's disease (AD), are classified into diffuse (associated with cognitive impairment) and dense-core types (a common finding in brains of people without Alzheimer's disease (non-AD) and without impaired cognitive function) based on their morphology. We tried to determine the usability of gray-level co-occurrence matrix (GLCM) texture parameters of homogeneity and heterogeneity for the differentiation of amyloid plaque images obtained from AD and non-AD individuals. Images of amyloid-β (Aβ) immunostained brain tissue samples were obtained from the Aging, Dementia and Traumatic Brain Injury Project. A total of 1,039 plaques were isolated from different brain regions of 69 AD and non-AD individuals and used for further GLCM analysis. Images of Aβ stained plaques show higher values of heterogeneity parameters and lower values of homogeneity parameters in AD patients, and vice versa in non-AD patients. Additionally, GLCM analysis shows differences in Aβ plaque texture between different brain regions in non-AD patients and correlates with variables that characterize patient's dementia status. The present study shows that GLCM texture analysis is an efficient method to discriminate between different types of amyloid plaques based on their morphology and thus can prove as a valuable tool in the neuropathological investigation of dementia.

Gray level co-occurrence matrix feature based object tracking in thermal infrared imagery

Object tracking plays a vital role in many computer vision systems applications, such as video surveillance, robotics, 3-D image reconstruction, medical imaging, human computer interface, etc. In many proposed approaches, feature-based object tracking is widely used due to its accuracy. Feature extraction and feature correspondence are two main components of feature-based object tracking. In our proposed method, we have used gray level co-occurrence matrix (GLCM) features for object tracking in thermal imagery. As spatial resolution of the thermal sensor is fairly coarse, it also implies that temperature scales are close but may not exactly be the same, which indicates the presence of mutually related pixels or group of pixels. The GLCM texture analysis is based on assumption that the texture information of an image is an average spatial relationship between the gray tones in the image. Thus, this similarity in spatial resolution properties makes GLCM features suitable for object tracking in thermal infrared imagery. Initially, the target blobs to be tracked are provided by object detection stage. Then, for a given target blob in a frame, we first calculate GLCM feature points and then find corresponding features in the next successive frame. The sum of squared differences between two feature point sets is calculated to find feature correspondence between two frames for object tracking. Simultaneously, the codebook of the center of the blobs for prediction of the target candidate region in the next frame is maintained in order to have robust object tracking under occlusions. GLCM-based object tracking in thermal imagery outperforms the color or LBP-based mean-shift approach. This algorithm is also able to track objects in split and merge condition. The accuracy of this algorithm also depends on the object detection stage, such as Kalman tracking.

Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation

Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk.

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

The Severity of Osteogenesis Imperfecta and Type I Collagen Pattern in Human Skin as Determined by Nonlinear Microscopy: Proof of Principle of a Diagnostic Method

BackgroundThe confirmatory diagnosis of Osteogenesis Imperfecta (OI) requires invasive, commonly bone biopsy, time consuming and destructive methods. This paper proposes an alternative method using a combination of two-photon excitation fluorescence (TPEF) and second-harmonic generation (SHG) microscopies from easily obtained human skin biopsies. We show that this method can distinguish subtypes of human OI.Methodology/Principal FindingsDifferent aspects of collagen microstructure of skin fresh biopsies and standard H&E-stained sections of normal and OI patients (mild and severe forms) were distinguished by TPEF and SHG images. Moreover, important differences between subtypes of OI were identified using different methods of quantification such as collagen density, ratio between collagen and elastic tissue, and gray-level co-occurrence matrix (GLCM) image-pattern analysis. Collagen density was lower in OI dermis, while the SHG/autofluorescence index of the dermis was significantly higher in OI as compared to that of the normal skin. We also showed that the energy value of GLCM texture analysis is useful to discriminate mild from severe OI and from normal skin.Conclusions/SignificanceThis work demonstrated that nonlinear microscopy techniques in combination with image-analysis approaches represent a powerful tool to investigate the collagen organization in skin dermis in patients with OI and has the potential to distinguish the different types of OI. The procedure outlined in this paper requires a skin biopsy, which is almost painless as compared to the bone biopsy commonly used in conventional methods. The data presented here complement existing clinical diagnostic techniques and can be used as a diagnostic procedure to confirm the disease, evaluate its severity and treatment efficacy.

Ultrasound GLCM texture analysis of radiation‐induced parotid‐gland injury in head‐and‐neck cancer radiotherapy: An in vivo study of late toxicity

Xerostomia (dry mouth), secondary to irradiation of the parotid glands, is one of the most common side effects of head-and-neck cancer radiotherapy. Diagnostic tools able to accurately and efficiently measure parotid gland injury have yet to be introduced into the clinic. This study's purpose is to investigate sonographic textural features as potential imaging signatures for quantitative assessment of parotid-gland injury after head-and-neck radiotherapy. The authors have investigated a series of sonographic features obtained from the gray level co-occurrence matrix (GLCM) - a second order statistical method of texture analysis. These GLCM textural features were selected based on empirical observations that the normal parotid gland exhibits homogeneous echotexture, whereas the postradiotherapy parotid gland often exhibits heterogeneous echotexture. We employed eight sonographic features: (1) angular second moment (ASM), (2) inverse differential moment (IDM), (3) contrast, (4) variance, (5) correlation, (6) entropy, (7) cluster shade, and (8) cluster prominence. Altogether, sonographic properties of the parotid glands were quantified by their degrees of homogeneity (ASM and IDM), heterogeneity (contrast and variance), smoothness (correlation), randomness (entropy), and symmetry (cluster shade and prominence). The sonographic features were tested in a pilot study of 12 postradiotherapy patients and 7 healthy volunteers. The mean follow-up time for the postradiotherapy patients was 17.2 months (range: 12.1-23.9 months) and the mean radiation dose to the parotid glands was 32.3 Gy (range: 11.0-63.4 Gy). Each participant underwent one ultrasound study in which longitudinal (vertical) ultrasound scans were performed on the bilateral parotids - a total of 24 postirradiation and 14 normal parotid glands were examined. The 14 normal parotid glands served as the control group. A radiologist contoured the parotid glands on the B-mode images and the sonographic features were computed from the contoured region-of-interest. The authors observed significant differences (p < 0.05) in all sonographic features between the normal and postradiotherapy parotid glands. The sonographic findings were consistent with the clinical observations of the ultrasound images: normal parotid glands exhibited homogeneous texture, while the postradiotherapy parotid glands exhibited heterogeneous echotexture (e.g., hyperechoic lines and spots), which likely represents fibrosis. The authors have demonstrated the feasibility of ultrasonic texture evaluation of parotid glands; and the sonographic features may serve as imaging signatures to assess radiation-induced parotid injury.