Density estimation of grey-level co-occurrence matrices for image texture analysis

Unveiling degenerative bone changes in the condyle: a texture analysis approach using cone-beam computed tomography

PURPOSE

The degenerative joint disease is a temporomandibular disorder. By analysing texture parameters, it becomes possible to characterize and differentiate various tissues, based on their textural properties according to cone-beam computed tomography (CBCT). This study evaluated degenerative diseases in the temporomandibular joint through texture analysis.

METHODS

Eighty images of the jaw condyle with three types of degenerative diseases, flattening, osteophytes, erosion and control group were analysed, obtained through CBCT. The analyses were carried out through texture analysis with three regions of interest (ROIs) corresponding to specific bone sites. The scans were exported to MaZda software, in which the ROIs were delimited following previously marked contours, and the co-occurrence matrix values were calculated for selected texture analysis parameters.

RESULTS

The erosion group showed a significantly different behaviour from the other groups for all analysed parameters.

CONCLUSION

This study highlights the potential of texture analysis in characterizing medullary bone changes in condyles affected by erosion. Texture analysis allows for a more comprehensive assessment of bone condition on CBCT images. These results have implications for early detection and monitoring of degenerative changes in the temporomandibular joint, thus allowing preventive intervention and personalized treatment planning, improving the prognosis of the disease.

Haralick texture feature analysis for Monte Carlo dose distributions of permanent implant prostate brachytherapy

PURPOSE

Demonstrate quantitative characterization of 3D patient-specific absorbed dose distributions using Haralick texture analysis, and interpret measures in terms of underlying physics and radiation dosimetry.

METHODS

Retrospective analysis is performed for 137 patients who underwent permanent implant prostate brachytherapy using two simulation conditions: "TG186" (realistic tissues including 0-3.8% intraprostatic calcifications; interseed attenuation) and "TG43" (water-model; no interseed attenuation). Five Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are calculated using the original Haralick formalism, and a modified approach designed to reduce grey-level quantization sensitivity. Trends in textural features are compared to clinical dosimetric measures (D90; minimum absorbed dose to the hottest 90% of a volume) and changes in patient target volume % intraprostatic calcifications by volume (%IC).

RESULTS

Both original and modified measures quantify the spatial differences in absorbed dose distributions. Strong correlations between differences in textural measures calculated under TG43 and TG186 conditions and %IC are observed for all measures. For example, differences between measures of contrast and correlation increase and decrease respectively as patients with higher levels of %IC are evaluated, reflecting the large differences across adjacent voxels (higher absorbed dose in voxels with calcification) when calculated under TG186 conditions. Conversely, the D90 metric is relatively weakly correlated with textural measures, as it generally does not characterize the spatial distribution of absorbed dose.

CONCLUSION

Patient-specific 3D dose distributions may be quantified using Haralick analysis, and trends may be interpreted in terms of fundamental physics. Promising future directions include investigations of novel treatment modalities and clinical outcomes.

Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis

This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Eleven GLCM parameters across five different distances were calculated. Median values and correlations of texture parameters were examined in relation to tumor differentiation grade by using Spearman's correlation coefficient and Kruskal-Wallis and Dunn tests. Forty-six patients were included, predominantly female (87%), with a mean age of 66.7 years. Texture analysis revealed significant parameter correlations with histopathological grades of tumor differentiation. The study identified no significant age correlation with tumor differentiation, which underscores the potential of texture analysis as an age-independent biomarker. The strong correlations between texture parameters and histopathological grades support the integration of this technique into the clinical decision-making process.

MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma

Purpose

To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).

Materials and methods

In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.

Results

Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.

Conclusion

Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.

Haralick texture analysis for microdosimetry: characterization of Monte Carlo generated 3D specific energy distributions

Objective.Explore the application of Haralick textural analysis to 3D distributions of specific energy (energy imparted per unit mass) scored in cell-scale targets considering varying mean specific energy (absorbed dose), target volume, and incident spectrum.Approach.Monte Carlo simulations are used to generate specific energy distributions in cell-scale water voxels ((1μm)3-(15μm)3) irradiated by photon sources (mean energies: 0.02-2 MeV) to varying mean specific energies (10-400 mGy). Five Haralick features (homogeneity, contrast, entropy, correlation, local homogeneity) are calculated using an implementation of Haralick analysis designed to reduce sensitivity to grey level quantization and are interpreted using fundamental radiation physics.Main results.Haralick measures quantify differences in 3D specific energy distributions observed with varying voxel volume, absorbed dose magnitude, and source spectrum. For example, specific energy distributions in small (1-3μm) voxels with low magnitudes of absorbed dose (10 mGy) have relatively high measures of homogeneity and local homogeneity and relatively low measures of contrast and entropy (all relative to measures for larger voxels), reflecting the many voxels with zero specific energy in an otherwise sporadic distribution. With increasing target size, energy is shared across more target voxels, and trends in Haralick measures, such as decreasing homogeneity and increasing contrast and entropy, reflect characteristics of each 3D specific energy distribution. Specific energy distributions for sources of differing mean energy are characterized by Haralick measures, e.g. contrast generally decreases with increasing source energy, correlation and homogeneity are often (not always) higher for higher energy sources.Significance.Haralick texture analysis successfully quantifies spatial trends in 3D specific energy distributions characteristic of radiation source, target size, and absorbed dose magnitude, thus offering new avenues to quantify microdosimetric data beyond first order histogram features. Promising future directions include investigations of multiscale tissue models, targeted radiation therapy techniques, and biological response to radiation.

Using Diffusion Tensor Imaging to Explore the Changes in the Microstructure of Canine Vocal Fold Scar Tissue

OBJECTIVE

To apply diffusion tensor imaging (DTI) in measurement of the diffusion characteristics of water molecules in vocal fold scar tissue, combined with the analysis of textural characteristics of collagen fibers in the cover layer of the vocal folds to explore the feasibility of DTI in the qualitative and quantitative diagnosis of vocal fold scars and the evaluation of microstructural changes of vocal fold scar tissue.

METHODS

A unilateral injury was created using micro-cup forceps in the left vocal fold of six beagles. The contralateral normal vocal fold was used as a self-control. Five months postinjury, the larynges were excised and placed into a magnetic resonance imaging (MRI) system (9.4T BioSpec MRI, Bruker, German) for scanning and extraction of the diffusion parameters, fractional anisotropy (FA) and tensor trace in the anterior, middle, and posterior portions of the vocal fold cover layer. These parameters were then analyzed for statistical significance between the scarred vocal fold and the normal vocal fold. After MRI scanning, the tissue of the vocal folds was divided into anterior, middle, and posterior parts for sectioning and staining with hematoxylin and eosin, and samples were subsequently digitally scanned for texture analysis. The irregularity parameters, energy, contrast, correlation, and homogeneity, of collagen fibers of the vocal folds and the mean gray value of collagen fibers were calculated by the gray-level co-occurrence matrix (GLCM) texture analysis method. The differences in the mean value of the two sides of the vocal fold were compared. In addition, Pearson correlation analysis was performed between DTI parameters and irregularity parameters.

RESULTS

The FA of the left vocal fold cover layer was significantly lower compared to the self-control group (P = 0.0366), and the tensor trace value on the left vocal fold cover layer was significantly higher compared to the self-control group (P = 0.0353). The FA was significantly higher in the anterior part of the right vocal fold cover layer compared to the middle and posterior parts of the same side (P = 0.0352), and the tensor trace was significantly lower in the anterior part of the right vocal fold cover layer compared to the middle and posterior parts of the same side (P = 0.0298). There were no significant differences in FA and tensor trace between the middle and posterior parts of the vocal fold cover layer. The mean gray value of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0219), the energy of the left vocal fold cover layer was significantly smaller than that of the right vocal fold cover layer (P < 0.0001), the contrast of the left vocal folds cover layer was significantly larger than that of the right vocal fold cover layer (P = 0.0002), the correlation of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0002), and the homogeneity of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0003). Pearson correlation analysis yielded values of r = 0.926, P = 0.000 between the FA and mean gray value; r = -0.918, P = 0.000 between FA and energy; r = -0.924, P = 0.000 between the FA and homogeneity, r = -0.949, P = 0.000 between tensor trace and mean gray value; r = 0.893, P = 0.000 between the tensor trace and energy; and r = 0.929, P = 0.000 between the tensor trace and homogeneity.

CONCLUSION

FA and tensor trace can be used as effective parameters to reflect microstructural changes in vocal fold scars. DTI is an objective and quantitative method of analyzing vocal fold scarring, and it noninvasively evaluates the microstructure of vocal fold collagen fibers.

Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.

Impact of neoadjuvant androgen deprivation therapy on magnetic resonance imaging features in prostate cancer before radiotherapy

Background and purpose

In locally advanced prostate cancer (PC), androgen deprivation therapy (ADT) in combination with whole prostate radiotherapy (RT) is the standard treatment. ADT affects the prostate as well as the tumour on multiparametric magnetic resonance imaging (MRI) with decreased PC conspicuity and impaired localisation of the prostate lesion. Image texture analysis has been suggested to be of aid in separating tumour from normal tissue. The aim of the study was to investigate the impact of ADT on baseline defined MRI features in prostate cancer with the goal to investigate if it might be of use in radiotherapy planning.

Materials and methods

Fifty PC patients were included. Multiparametric MRI was performed before, and three months after ADT. At baseline, a tumour volume was delineated on apparent diffusion coefficient (ADC) maps with suspected tumour content and a reference volume in normal prostatic tissue. These volumes were transferred to MRIs after ADT and were analysed with first-order -and invariant Haralick -features.

Results

At baseline, the median value and several of the invariant Haralick features of ADC, showed a significant difference between tumour and reference volumes. After ADT, only ADC median value could significantly differentiate the two volumes.

Conclusions

Invariant Haralick -features could not distinguish between baseline MRI defined PC and normal tissue after ADT. First-order median value remained significantly different in tumour and reference volumes after ADT, but the difference was less pronounced than before ADT.

Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features

PURPOSE

The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images.

METHODS

Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design.

RESULTS

The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination.

CONCLUSION

Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.