Gray Level Co-occurrence Matrix Parameters Research Articles

The Fractal and GLCM Textural Parameters of Chromatin May Be Potential Biomarkers of Papillary Thyroid Carcinoma in Hashimoto's Thyroiditis Specimens.

Occasionally, Hashimoto's thyroiditis (HT) and papillary thyroid carcinoma (PTC) share similar nuclear features. The current study aims to quantify the differences between the investigated specimens of HT-associated PTC versus the HT alone, to reduce the subjective experience of an observer, by the use of fractal parameters as well as gray-level co-occurrence matrix (GLCM) textural parameters. We have analyzed 250 segmented nuclei per group (nn = 25 per patient and np = 10 patients per group) using the ImageJ software (NIH, Bethesda, MD, USA) as well as an in-house written code for the GLCM analysis. The mean values of parameters were calculated for each patient. The results demonstrated that the malignant cells from the HT-associated PTC specimens showed lower chromatin fractal dimension (p = 0.0321) and higher lacunarity (p = 0.0038) compared with the corresponding cells from the HT specimens. Also, there was a statistically significant difference between the investigated specimens, in the contrast, correlation, angular second moment, and homogeneity, of the GLCM corresponding to the visual texture of follicular cell chromatin. The differences in chromatin fractal and GLCM parameters could be integrated with other diagnostic methods for the improved evaluation of distinctive features of the HT-associated PTC versus the HT in cytology and surgical pathology specimens.

Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps.

BACKGROUNDIt is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning. It has not been extensively investigated whether texture features derived from diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps are associated with the extent of local invasion (pathological stage T1-2 vs T3-4) and nodal involvement (pathological stage N0 vs N1-2) in rectal cancer.AIMTo predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODSOne hundred and fifteen patients with pathologically proven rectal cancer, who underwent preoperative magnetic resonance imaging, including DWI, were enrolled, retrospectively. The ADC measurements (ADCmean, ADCmin, ADCmax) as well as texture features, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI (b = 0 and b = 1000) images and the ADC maps. Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis. Multivariate logistic regression analysis was conducted to establish the models. The predictive performance was validated by receiver operating characteristic curve analysis. RESULTSDissimilarity, sum average, information correlation and run-length nonuniformity from DWIb=0 images, gray level nonuniformity, run percentage and run-length nonuniformity from DWIb=1000 images, and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion (stage T3-4). The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57% and a specificity of 74.19%. Sum average, gray level nonuniformity and the horizontal components of symlet transform (SymletH) from DWIb=0 images, sum average, information correlation, long run low gray level emphasis and SymletH from DWIb=1000 images, and ADCmax, ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.CONCLUSIONTexture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.

Gray Level Co-Occurrence Matrix (GLCM) Parameters Analysis for Pyoderma Image Variants

Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLCM) based texture analysis of skin diseases for parametric variations. The investigations were carried out using three Pyoderma variants (Boil, Carbuncle, and Impetigo Contagiosa) using GLCM. GLCM parameters (Energy, Correlation, Contrast, and Homogeneity) were extracted for each colour component of the images taken for the investigation. Contrast, correlation, energy, and homogeneity represent the coarseness, linear dependency, textural uniformity, and pixel distribution of the texture, respectively. The analysis of the GLCM parameters and their histograms showed that the said textural features are disease dependent. The approach may be used for the identification of dermatological diseases with satisfactory accuracy by employing a suitable machine learning algorithm.

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PurposeGlioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. MethodIn a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. ResultsAll five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. ConclusionsComputational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Application of Gray Level co-Occurrence Matrix Algorithm for Detection of Discrete Structural Changes in Cell Nuclei After Exposure to Iron Oxide Nanoparticles and 6-Hydroxydopamine.

The gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

Chromatin textural parameters of blood neutrophils are associated with stress levels in patients with recurrent depressive disorder

Introduction/Objective. During the past 20 years, there have been numerous attempts to design and apply a simple, affordable blood analysis tool for diagnostic and prognostic purposes in psychiatry. In this article we demonstrate that some mathematical parameters of chromatin organization and distribution in blood neutrophil granulocytes are related to stress levels in patients diagnosed with recurrent depressive disorder (RDD). Methods. The study was performed on 50 RDD participants who were asked to complete Depression, Anxiety and Stress Scales (DASS-21). Peripheral blood samples were obtained from all the participants, smeared on glass slides and stained using a modification of Giemsa method. A total of 500 representative chromatin structures (10 per patient) of neutrophil granulocytes were evaluated using textural analysis with the application of gray level co-occurrence matrix (GLCM) method. Parameters such as angular second moment (indicator of textural uniformity), inverse difference moment (textural homogeneity), and textural sum variance were calculated. Results. The results indicate that there is a statistically highly significant correlation (p < 0.01) between certain chromatin GLCM parameters such as inverse difference moment, and DASS-21 stress score. There was also a significant difference (p < 0.05) in some chromatin GLCM parameters in patients diagnosed with RDD with psychotic features, when compared to the ones without psychosis. Conclusion. These findings suggest that in the future, chromatin GLCM features might have a certain predictive value for some clinical features of recurrent depressive disorder.

Rapid classification of micron-sized particles of sphere, cylinders and ellipsoids by diffraction image parameters combined with scattered light intensity

Spatial distributions of light scattered by single particles correlate closely with their morphologies in terms of refractive index (RI) distribution. Diffraction imaging of scattered light under coherent excitation presents a unique approach to acquire and extract feature parameters for particle classification. A validated method has been applied in this study to accurately simulate diffraction imaging of light scattered by homogeneous particles and obtain calculated diffraction image (DI) data. The feature parameters of DI data have been extracted by the gray-level co-occurrence matrix (GLCM) algorithm. We have developed an unsupervised machine learning algorithm based on Gaussian mixture model (GMM) to classify 1965 particles made of single and double spheres, cylinders and ellipsoids with varied RI values in parameter space. It has been shown that selected GLCM parameters combined with integrated forward scatter intensity can provide effective markers for accurate and morphology based classification. For 1791 particles of the same RI, the mean accuracy values of classifying particles into 3 particle types range from 82.6% to 97.2%. These results demonstrate the strong potential of diffraction imaging method for rapid analysis and classification of nonspherical and homogeneous particles by the GMM classifiers that is very challenging in comparison to distinguishing biological cell types.

Second-harmonic generation imaging analysis can help distinguish sarcoidosis from tuberculoid leprosy.

Sarcoidosis and tuberculoid leprosy (TL) are prototypes of granulomatous inflammation in dermatology, which embody one of the histopathology limitations in distinguishing some diseases. Recent advances in the use of nonlinear optical microscopy in skin have enabled techniques, such as second-harmonic generation (SHG), to become powerful tools to study the physical and biochemical properties of skin. We use SHG images to analyze the collagen network, to distinguish differences between sarcoidosis and TL granulomas. SHG images obtained from skin biopsies of 33 patients with TL and 24 with sarcoidosis retrospectively were analyzed using first-order statistics (FOS) and second-order statistics, such as gray-level co-occurrence matrix (GLCM). Among the four parameters evaluated (optical density, entropy, contrast, and second angular moment), only contrast demonstrated statistical significance, being higher in sarcoidosis (p = 0.02; 4908.31 versus 2822.17). The results may indicate insufficient differentiating power for most tested FOS and GLCM parameters in classifying sarcoidosis and TL granulomas, when used individually. But in combination with histopathology (H&E and complementary stains, such as silver and fast acid stains), SHG analysis, like contrast, can contribute to distinguishing between these diseases. This study can provide a way to evaluate collagen distribution in granulomatous diseases.

Variation in algorithm implementation across radiomics software.

Given the increased need for consistent quantitative image analysis, variations in radiomics feature calculations due to differences in radiomics software were investigated. Two in-house radiomics packages and two freely available radiomics packages, MaZda and IBEX, were utilized. Forty regions of interest (ROIs) from 40 digital mammograms were studied along with 39 manually delineated ROIs from the head and neck (HN) computed tomography (CT) scans of 39 patients. Each package was used to calculate first-order histogram and second-order gray-level co-occurrence matrix (GLCM) features. Friedman tests determined differences in feature values across packages, whereas intraclass-correlation coefficients (ICC) quantified agreement. All first-order features computed from both mammography and HN cases (except skewness in mammography) showed significant differences across all packages due to systematic biases introduced by each package; however, based on ICC values, all but one first-order feature calculated on mammography ROIs and all but two first-order features calculated on HN CT ROIs showed excellent agreement, indicating the observed differences were small relative to the feature values but the bias was systematic. All second-order features computed from the two databases both differed significantly and showed poor agreement among packages, due largely to discrepancies in package-specific default GLCM parameters. Additional differences in radiomics features were traced to variations in image preprocessing, algorithm implementation, and naming conventions. Large variations in features among software packages indicate that increased efforts to standardize radiomics processes must be conducted.

Feature Analysis of Kidney Ultrasound Image in Four Different Ultrasound using Gray Level Co-occurrence Matrix (GLCM) and Intensity Histogram (IH)

Misinterpretation analysis of ultrasound images has been huge issues in the world nowadays. Lack of skills and knowledge, as well as unclear ultrasound image due to the presence of speckle noise in ultrasound, are some factors lead to this issue. In this research, we compare 188 kidney ultrasound images from four different types of ultrasound machines, named as ultrasound A, B, C and D. Image pre-processing of images which involve cropping, enhancement, and filtering are performed before manual segmentation and texture analysis process to indicates the wanted region and improve contrast in each image. Texture analysis is performed using gray level co-occurrence matrix (GLCM) and intensity histogram (IH) to find differences and similarities in kidney image texture between all ultrasounds. Four GLCM parameters, contrast, correlation, energy and homogeneity and four parameters from IH (mean, standard deviation, variance, and skewness) used to indicate the most significant features between all ultrasound machines. Results show that contrast in GLCM is the most significant features that can be extracted from all four ultrasound machines and will be used in the classification process.

Label-Free Imaging of Melanoma with Confocal Photothermal Microscopy: Differentiation between Malignant and Benign Tissue

Label-free confocal photothermal (CPT) microscopy was utilized for the first time to investigate malignancy in mouse skin cells. Laser diodes (LDs) with 405 nm or 488 nm wavelengths were used as pumps, and a 638 nm LD was used as a probe for the CPT microscope. A Grey Level Cooccurrence Matrix (GLCM) for texture analysis was applied to the CPT images. Nine GLCM parameters were calculated with definite definitions for the intracellular super-resolved CPT images, and the parameters Entropy, Contrast, and Variance were found to be most suited among the nine parameters to discriminate clearly between healthy cells and malignant cells when a 405 nm pump was used. Prominence, Variance, and Shade were most suited when a pump wavelength of 488 nm was used.

Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt

C-band synthetic aperture radar (SAR) is widely used for sea ice monitoring and operational activities. The RADARSAT Constellation Mission (RCM), with its anticipated launch in 2018, will provide hybrid compact polarimetric (CP) C-band SAR data offering near-polarimetric capabilities at large image acquisition widths suitable for achieving operational and scientific objectives in the Arctic. Although C-band SAR is effective for sea ice monitoring, it is difficult to implement during advanced melt, when the sea ice cover is melting and covered by melt ponds. Ice type separability during winter (pre-melt) and advanced melt conditions was assessed using Kolmogorov–Smirnov statistical separability analyses and Support Vector Machine supervised classifications of RCM parameters simulated from 2 winter and 2 advanced melt RADARSAT-2 scenes. Through a detailed analysis of the 2 advanced melt scenes, it was found that the steep incidence angle (22.3–24.2°) simulated RCM CP parameters provide improved ice type separability during the advanced melt period compared with shallow incidence angles (39.6–42.2°). With respect to classification, an overall accuracy of 77.06% was found for a scene comprising first-year and multiyear ice types, and a higher overall accuracy of 85.91% was achieved by including gray level co-occurrence matrix parameters in the classification.

Texture analysis of protein deposits produced by droplet evaporation

The deposit patterns derived from droplet evaporation allow current development of medical tests and new strategies for diagnostic in patients. For such purpose, the development and implementation of algorithms capable of characterizing and differentiating deposits are crucial elements. We report the study of deposit patterns formed by the droplet evaporation of binary mixtures of proteins containing NaCl. Optical microscopy reveals aggregates such as tip arrow-shaped, dendritic and semi-rosette patterns, needle-like and scalloped lines structures, as well as star-like and prism-shaped salt crystals. We use the first-order statistics (FOS) and gray level co-occurrence matrix (GLCM) to characterize the complex texture of deposit patterns. Three significant findings arise from this analysis: first, the FOS and GLCM parameters structurally characterize protein deposits. Secondly, they conform to simple exponential laws that change as a function of the NaCl concentration. Finally, the parameters are capable of revealing the different structural changes that occur during the droplet evaporation.

The Effects of Betaine on the Nuclear Fractal Dimension, Chromatin Texture, and Proliferative Activity in Hepatocytes in Mouse Model of Nonalcoholic Fatty Liver Disease.

The effects of betaine on hepatocytes chromatin architecture changes were examined by using fractal and gray-level co-occurrence matrix (GLCM) analysis in methionine/choline-deficient (MCD) diet-induced, nonalcoholic fatty liver disease (NAFLD). Male C57BL/6 mice were divided into groups: (1) Control: standard diet; (2) BET: standard diet and betaine supplementation through drinking water (solution 1.5%); (3) MCD group: MCD diet for 6 weeks; (4) MCD+BET: fed with MCD diet + betaine for 6 weeks. Liver tissue was collected for histopathology, immunohistochemistry, and determination of fractal dimension and GLCM parameters. MCD diet induced diffuse micro- and macrovesicular steatosis accompanied with increased Ki67-positive hepatocyte nuclei. Steatosis and Ki67 immunopositivity were less prominent in the MCD+BET group compared with the MCD group. Angular second moment (ASM) and inverse difference moment (IDM) (textural homogeneity markers) were significantly increased in the MCD+BET group versus the MCD group (p<0.001), even though no difference between the MCD and the control group was evident. Heterogeneity parameters, contrast, and correlation were significantly increased in the MCD group versus the control (p<0.001). On the other hand, betaine treatment significantly reduced correlation, contrast, and entropy compared with the MCD group (p<0.001). Betaine attenuated MCD diet-induced NAFLD by reducing fat accumulation and inhibiting hepatocyte proliferation. Betaine supplementation increased nuclear homogeneity and chromatin complexity with reduction of entropy, contrast, and correlation.

Postnatal Developmental Changes in Fractal Complexity of Giemsa-Stained Chromatin in Mice Spleen Follicular Cells.

Although there are numerous recent works focusing on fractal properties of DNA and chromatin, many issues regarding changes in chromatin fractality during physiological aging remain unclear. In this study, we present results indicating that in mice, there is an age-related reduction of chromatin fractal complexity in a population of spleen follicular cells (SFCs). Spleen tissue was obtained from 16 mice and fixated in Carnoy solution. The youngest animal was newborn, and each animal was exactly 1 month older than the previous. We performed fractal analysis of SFC chromatin structure, stained using Giemsa technique. Fractal analysis was done in a plugin algorithm of ImageJ software. We also performed gray-level co-occurrence matrix (GLCM) analysis of all chromatin structures with the calculation of parameters such as angular second moment and inverse difference moment. Giemsa-stained SFC chromatin exhibited an age-dependent reduction of fractal dimension with statistically significant (p<0.01) linear trend. Moreover, there was a statistically significant increase of SFC chromatin lacunarity. The chromatin GLCM parameters did not significantly change. To our knowledge, this is the first study to perform fractal and GLCM analyses of SFC chromatin and to investigate potential changes of fractal parameters during postnatal development.

An optimized skin texture model using gray-level co-occurrence matrix

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection.

Quantitative Muscle Ultrasonography Using Textural Analysis in Amyotrophic Lateral Sclerosis.

The purpose of this study was to analyze differences in gray-level co-occurrence matrix (GLCM) parameters, as assessed by muscle ultrasound (MUS), between amyotrophic lateral sclerosis (ALS) patients and healthy controls, and to compare the diagnostic accuracy of these GLCM parameters with first-order MUS parameters (echointensity [EI], echovariation [EV], and muscle thickness [MTh]) in different muscle groups. Twenty-six patients with ALS and 26 healthy subjects underwent bilateral and transverse ultrasound of the biceps/brachialis, forearm flexor, quadriceps femoris, and tibialis anterior muscle groups. MTh was measured with electronic calipers, and EI, EV, and GLCM were obtained using Image J (v.1.48) software. Sensitivity, specificity, likelihood ratios, and area under the curve (AUC) were performed by logistic regression models and receiver operating characteristic curves. GLCM parameters showed reduced granularity in the muscles of ALS patients compared with the controls. Regarding the discrimination capacity, the best single diagnostic parameter in forearm flexors and quadriceps was GLCM and in biceps brachialis and tibialis anterior was EV. The respective combination of these two parameters with MTh resulted in the best AUC (over 90% in all muscle groups and close to the maximum combination model). The use of new textural parameters (EV and GLCM) combined with usual quantitative MUS variables is a promising biomarker in ALS.

Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy.

Qualifications of intracellular structure were performed for the first time using the gray-level co-occurrence matrix (GLCM) method for images of cells obtained by resolution-enhanced photothermal imaging. The GLCM method has been used to extract five parameters of texture features for five different types of cells in mouse brain; pyramidal neurons and glial cells in the basal nucleus (BGl), dentate gyrus granule cells, cerebellar Purkinje cells, and cerebellar granule cells. The parameters are correlation, contrast, angular second moment (ASM), inverse difference moment (IDM), and entropy for the images of cells of interest in a mouse brain. The parameters vary depending on the pixel distance taken in the analysis method. Based on the obtained results, we identified that the most suitable GLCM parameter is IDM for pyramidal neurons and BGI, granule cells in the dentate gyrus, Purkinje cells and granule cells in the cerebellum. It was also found that the ASM is the most appropriate for neurons in the basal nucleus.

Chromatin Fractal Organization, Textural Patterns, and Circularity of Nuclear Envelope in Adrenal Zona Fasciculata Cells.

Despite previous research efforts in the fields of histology and cell physiology, the relationship between chromatin structural organization and nuclear shape remains unclear. The aim of this research was to test the existence and strength of correlations between mathematical parameters of chromatin microarchitecture and roundness of the nuclear envelope. On a sample of 240 nuclei of adrenal zona fasciculata cells stained using the DNA-specific Feulgen method, we quantified fractal parameters such as fractal dimension and lacunarity, as well as textural parameters such as angular second moment (ASM), entropy, inverse difference moment, contrast, and variance. Circularity of the nuclear envelope was determined from the nuclear area and perimeter. The results indicate that there is a statistically significant negative correlation between chromatin ASM and circularity. Moreover, there was a statistically significant positive correlation between chromatin fractal dimension and envelope circularity. This is the first study to demonstrate these relationships in adrenal tissue, and also one of the first studies to test the connection between circularity and fractal and gray-level co-occurrence matrix parameters in DNA-specific Feulgen stain. The results could be useful both as an addition to the current knowledge on chromatin/nuclear envelope interactions, and for design of future computer-assisted research software for evaluation of nuclear morphology.