Gray-level Co-occurrence Features Research Articles

Drone lidar-derived surface complexity metrics as indicators of intertidal oyster reef condition

Eastern oysters (Crassostrea virginica) generate structurally complex reef systems that offer diverse ecosystem services. However, there is limited understanding of how reef structure translates into reef condition. This knowledge gap might be better addressed if oyster reef structure could be more rapidly assessed. Conventional in situ monitoring techniques are often time-intensive, invasive, and do not provide spatially continuous information on the reef structure. Unoccupied Aircraft Systems (UAS), commonly referred to as drones, equipped with optical sensors can rapidly and non-invasively map intertidal oyster reef surfaces. We demonstrate how a digital surface model from UAS-based light detection and ranging (lidar) can enable very high-resolution characterization and monitoring of intertidal oyster reef surface morphology. Generalized linear models (GLMs) identified relationships between in situ live oyster counts and surface complexity metrics derived from digital surface models produced from lidar point clouds. Statistically significant relationships between surface complexity metrics (e.g., gray level co-occurrence features, volume to area ratio, skewness of elevation) and live oyster counts suggest that surface complexity provides useful proxies for reef condition. Advancing the application of remote sensing to intertidal oyster reefs can help identify reefs that are prone to degradation and inform conservation and restoration strategies.

Quality control of radiomic features using 3D-printed CT phantoms.

.Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting.Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes.Results: The structural similarity index between the segment used from the patients’ DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with .Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.

CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma.

Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [18F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [18F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [18F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity. Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29-62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [18F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups. Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017-0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001-.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes. CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.

Abstract P4-01-02: Identification of histopathologic determinants of mammographic breast density as a cancer risk factor

Abstract Background: Mammographic density and texture, the amount and appearance of fibroglandular tissue respectively, are known to be strong predictors of a woman's risk to develop breast cancer. However, how differences in underlying tissue biology are associated with the wide range of breast density patterns seen among women is currently unknown. Understanding the biological mechanisms driving breast density can be critical for developing targeted interventions aimed at reducing a woman's risk for breast cancer. Methods: Digital mammograms and formalin-fixed paraffin-embedded biopsy tissue sections from 73 women who donated breast tissue to the Susan G. Komen for the Cure Tissue Bank at the Indiana University Simon Cancer Center were included in this analysis. Hematoxylin and eosin (H&amp;E) and AE1/AE3 cytokeratin stains were applied to the tissue sections, which were then visually characterized by an expert breast pathologist. H&amp;E stains were used to assess the presence (in ≥5% of the tissue section) or absence of non-proliferative fibrocystic changes, proliferative fibrocystic changes without atypia, inflammatory and reactive conditions, or benign tumors. AE1/AE3 stains were used to quantify the extent of epithelial and stromal content, as well as the number of lobules and ductal structures seen in the section. Quantitative breast percent density (PD%) and whole-breast texture analysis was performed on the digital mammograms using validated software. Parenchymal texture measures recently demonstrated to be associated with breast cancer risk were assessed, including the gray-level co-occurrence features Cluster-shade, Correlation, Energy, Entropy, Inertia and Inverse Difference Moment. Texture was characterized within a 2.5cm2 area of interest in the upper-outer quadrant of the mammograms corresponding to the approximate location of the biopsy. Partial-R2 (pR2) analysis was applied in order to assess the proportion of the variability in the mammographic texture which can be directly attributed to underlying histological properties after adjusting for race, menopausal status, body mass index, PD% and mammography unit manufacturer, utilizing a false-discovery-rate (FDR) multiple comparisons correction. Results: The presence of proliferative fibrocystic changes without atypia was found to have a significant association to Correlation (pR2=0.21), Energy (pR2=0.13), Entropy (pR2=0.16), and Inverse Difference Moment (pR2=0.14) at the FDR-corrected significance level of p&amp;lt;0.0042, even after adjusting for race, menopausal status, body mass index, PD% and the mammography unit manufacturer. These findings suggest that 13-21% of the variation in parenchymal texture patterns seen between women can be explained by the presence of proliferative fibrocystic changes without atypia, a common and well known risk factor for breast cancer. Conclusion: In this study, we identified proliferative fibrocystic changes without atypia as one potential driver of image-based biomarkers of breast cancer risk. Ultimately, identification of radiographic biomarkers of tissue microenvironment could aid in the development of targeted chemopreventative interventions for women with dense breast tissue while simultaneously providing image-based biomarkers to monitor their efficacy. Citation Format: Keller BM, Batiste RC, Chen J, McDonald ES, Conant EF, Kontos D, Feldman MD. Identification of histopathologic determinants of mammographic breast density as a cancer risk factor. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-01-02.

Citrus Huanglongbing Detection Using Narrow-Band Imaging and Polarized Illumination

Abstract. The insect-spread bacterial infection known as citrus greening or Huanglongbing (HLB) is a very destructive citrus disease and has caused massive losses in Florida’s citrus industry. Early, easy, and less expensive HLB detection based on particular symptoms, such as starch accumulation in the citrus leaf, would increase the chance of preventing the disease from being spread and causing more damage. The ability of narrow-band imaging and polarizing filters in detecting starch accumulation in symptomatic citrus leaf was evaluated in this study. A custom-made image acquisition system was developed for this purpose in which leaf samples were illuminated with polarized light using narrow-band high-power LEDs at 400 nm and 591 nm, and the reflectance was measured by two monochrome cameras. Two polarizing filters were mounted in perpendicular directions in front of the cameras so that each camera acquired an image with reflected light in only one direction (parallel or perpendicular to the illumination polarization). Four groups of textural features, including gray, local binary pattern, local similarity pattern, and gray-level co-occurrence features, were extracted and ranked using several feature selection methods. Seven classifiers (support vector machine, linear, naive Bayes linear, quadratic, naive Bayes quadratic, Mahalanobis, and k nearest neighbor) were evaluated, and the best classifiers and sets of features were selected based on their accuracy. The leaf samples were collected from the ‘Hamlin’ and ‘Valencia’ varieties of citrus. Three classes of samples (magnesium-deficient, HLB-positive zinc-deficient, and HLB-negative zinc-deficient) were considered in the classification process to confirm the starch detection ability of the system. Overall average accuracies of 93.1% and 89.6% in HLB detection were obtained for the ‘Hamlin’ and ‘Valencia’ varieties, respectively, using a step-by-step classification method. The results of this study showed that the starch accumulation in HLB-symptomatic leaves rotated the polarization planar of light at 591 nm, and this property can be effectively used in a fast and inexpensive HLB detection system.

PERFORMANCE ANALYSIS OF BRAIN TUMOR DIAGNOSIS BASED ON SOFT COMPUTING TECHNIQUES

Computed tomography images are widely used in the diagnosis of brain tumor because of its faster processing, avoiding malfunctions and suitability with physician and radiologist. This study proposes a new approach to automated detection of brain tumor. This proposed work consists of various stages in their diagnosis processing such as preprocessing, anisotropic diffusion, feature extraction and classification. The local binary patterns and gray level co-occurrence features, gray level and wavelet features are extracted and these features are trained and classified using Support vector machine classifier. The achieved results and quantitatively evaluated and compared with various ground truth images. The proposed method gives fast and better segmentation and classification rate by yielding 99.4% of sensitivity, 99.6% of specificity, 97.03% of positive predictive value and 99.5% of overall accuracy.