Proceedings of the NASA Workshop on Image Analysis
Three major topics of image analysis are addressed: segmentation, shape and texture analysis, and structural analysis.
Guseman, L. F., Jr.↗
SEARCH · Engineering Papers
Results for “texture segmentation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989
Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.
Tescher, Andrew G.↗
Generation of texture images using adaptive windows
Simulated forest and observed TM and SPOT images illustrate the improvements an adaptive-window texture image can have over traditional texture images for applications like image segmentation where edge effects in the texture image can be detrimental. By adaptive selection of the 'best' window position (as opposed to always having the window centered over the pixel) minimizes edge effects while retaining local texture information. Tests using 3 x 3 windows and local variance on SPOT, TM and simulated TM data show similar results.
Woodcock, Curtis E.↗
Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest
Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.
60 APPLIED LIFE SCIENCES↗
Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy
Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment.
Yoon, Hong-Jun↗
Mechanical and chemical effects of ion-texturing biomedical polymers
To determine whether sputter etching may provide substantial polymer surface texturing with insignificant changes in chemical and mechanical properties, an 8 cm beam diameter, electron bombardment, argon ion source was used to sputter etch (ion-texture process) nine biomedical polymers. The materials included silicone rubber, 32% carbon impregnated polyolefin, polyoxymethylene, polytetrafluoroethylene, ultrahigh molecular weight (UHMW) polyethylene, UHMW polyethylene with carbon fibers (10%), and several polyurethanes (bioelectric, segmented, and cross linked). Ion textured microtensile specimens of each material except UHMW polyethylene and UHMW polyethylene with 10% carbon fibers were used to determine the effect of ion texturing on tensile properties. Scanning electron microscopy was used to determine surface morphology changes, and electron spectroscopy for chemical analysis was used to analyze the near surface chemical changes that result from ion texturing. Ion energies of 500 eV with beam current densities ranging from 0.08 to 0.19 mA/sq cm were used to ion texture the various materials. Standard microtensile specimens of seven polymers were exposed to a saline environment for 24 hours prior to and during the tensile testing. The surface chemical changes resulting from sputter etching are minimal in spite of the often significant changes in the surface morphology.
Weigand, A. J.↗
Intradomain Textures in Block Copolymers: Multizone Alignment and Biaxiality
Block copolymer (BCP) melt assembly has been studied for decades, focusing largely on self-organized spatial patterns of periodically ordered segment density. Here, we demonstrate that underlying the well-known composition profiles (i.e., ordered lamella, cylinders, spheres, and networks) are generic and heterogeneous patterns of segment orientation that couple strongly to morphology, even in the absence of specific factors that promote intra or interchain segment alignment. We employ both self-consistent field theory and coarse-grained simulation methods to measure polar and nematic order parameters of segments in a freely jointed chain model of diblock melts. We show that BCP morphologies have a multizone texture, with segments predominantly aligned normal and parallel to interdomain interfaces in the respective brush and interfacial regions of the microdomain. Further, morphologies with anisotropically curved interfaces (i.e., cylinders and networks) exhibit biaxial order that is aligned to the principal curvature axes of the interface.
42 ENGINEERING↗
SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT
Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysis was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.
60 APPLIED LIFE SCIENCES↗
Analysis of Texture Using the Fractal Model
Properties such as the fractal dimension (FD) can be used for feature extraction and classification of regions within an image. The FD measures the degree of roughness of a surface, so this number is used to characterize a particular region, in order to differentiate it from another. There are two basic approaches discussed in the literature to measure FD: the blanket method, and the box counting method. Both attempt to measure FD by estimating the change in surface area with respect to the change in resolution. We tested both methods but box counting resulted computationally faster and gave better results. Differential Box Counting (DBC) was used to segment a collage containing three textures. The FD is independent of directionality and brightness so five features were used derived from the original image to account for directionality and gray level biases. FD can not be measured on a point, so we use a window that slides across the image giving values of FD to the pixel on the center of the window. Windowing blurs the boundaries of adjacent classes, so an edge-preserving, feature-smoothing algorithm is used to improve classification within segments and to make the boundaries sharper. Segmentation using DBC was 90.8910 accurate.
Navas, William↗
SU-E-J-113: Effects of Deformable Registration On First-Order Texture Maps Calculated From Thoracic Lung CT Scans
Purpose: To determine the stability of eight first-order texture features following the deformable registration of serial computed tomography (CT) scans. Methods: CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in-house program, texture maps were calculated from 32×32-pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lung were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow-up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow-up scans. Bland-Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value. Results: Each patient’s scans contained between 6.8–15.4 million ROIs. All of the first-order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low. Conclusion: The eight first-order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal-spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient-by-patient basis. SGA and HA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R25GM109439.
60 APPLIED LIFE SCIENCES↗
SU-E-J-248: Contributions of Tumor and Stroma Phenotyping in Computer-Aided Diagnosis
Purpose: To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions. Methods: This study was performed on 182 biopsy-proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full-field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region-of-interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs to assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave-one-lesion-out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit. Results: Lesion characterization included computer-extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer-selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions. Conclusion: Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making. Funding: University of Chicago Dean Bridge Fund, NCI U24-CA143848-05, P50-CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights.
60 APPLIED LIFE SCIENCES↗
Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis
Purpose: To predict the rate of liver regeneration after living donor liver transplantation (LDLT) using pre-operative computed tomography (CT) texture analysis. Materials and methods: 112 living donors who performed right hepatectomy for LDLT were included retrospectively. We measured the volume of future remnant liver (FLR) on pre-operative CT and the volume of remnant liver (LR) on follow-up CT, taken at a median of 123 days after transplantation. The regeneration index (RI) was calculated using the following equation: [(V{sub LR}−V{sub FLR})/V{sub FLR}] × 100. Computerized texture analysis of the semi-automatically segmented FLR was performed. We used a stepwise, multivariable linear regression to assess associations of clinical features and texture parameters in relation to RI and to make the best-fit predictive model. Results: The mean RI was 110.7 ± 37.8%, highly variable ranging from 22.4% to 247.0%. Among texture parameters, volume of FLR, standard deviation, variance, and gray level co-occurrence matrices (GLCM) contrast were found to have significant correlations between RI. In multivariable analysis, smaller volume of FLR (ß − 0.17, 95% CI − 0.22 to − 0.13) and lower GLCM contrast (ß − 1.87, 95% CI − 3.64 to − 0.10) were associated with higher RI. The regression equation predicting RI was following: RI = 203.82 + 10.42 × pre-operative serum total bilirubin (mg/dL) − 0.17 × V{sub FLR} (cm{sup 3}) − 1.87 × GLCM contrast (× 100). Conclusion: Volume of FLR and GLCM contrast were independent predictors of RI, showing significant negative correlations. Pre-operative CT with texture analysis can be useful for predicting the rate of liver regeneration in living donor of liver transplantation.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Classification Experiments on Real-World Texture
Many papers have been published concerning the analysis of visual texture and yet, very few application domains use texture for image classification. A possible reason for this low transfer of the technology is the lack of experience and testing in real-world imagery. In this paper, we assess the performance of texture-based classification methods on a number of real-world images relevant to autonomous navigation on cross-country terrain and to autonomous geology. Texture analysis will form part of the closed loop that allows a robotic system to navigate autonomously. We have implemented two different classifiers on features extracted by Gabor filter banks. The first classifier models feature distributions for each texture class using a mixture of Gaussians. Classification is performed using Maximum Likelihood. The second classifier represents local statistics using marginal histograms of the features over a region centered on the pixel to be classified. We measure system performance by comparison to ground truth image labels.
image segmentation↗
Spatial intratumoral heterogeneity of proliferation in immunohistochemical images of solid tumors
Purpose: The interactions of neoplastic cells with each other and the microenvironment are complex. To understand intratumoral heterogeneity, subtle differences should be quantified. Main factors contributing to heterogeneity include the gradient ischemic level within neoplasms, action of microenvironment, mechanisms of intercellular transfer of genetic information, and differential mechanisms of modifications of genetic material/proteins. This may reflect on the expression of biomarkers in the context of prognosis/stratification. Hence, a rigorous approach for assessing the spatial intratumoral heterogeneity of histological biomarker expression with accuracy and reproducibility is required, since patterns in immunohistochemical images can be challenging to identify and describe. Methods: A quantitative method that is useful for characterizing complex irregular structures is lacunarity; it is a multiscale technique that exhaustively samples the image, while the decay of its index as a function of window size follows characteristic patterns for different spatial arrangements. In histological images, lacunarity provides a useful measure for the spatial organization of a biomarker when a sampling scheme is employed and relevant features are computed. The proposed approach quantifies the segmented proliferative cells and not the textural content of the histological slide, thus providing a more realistic measure of heterogeneity within the sample space of the tumor region. The aim is to investigate in whole sections of primary pancreatic neuroendocrine neoplasms (pNENs), using whole-slide imaging and image analysis, the spatial intratumoral heterogeneity of Ki-67 immunostains. Unsupervised learning is employed to verify that the approach can partition the tissue sections according to distributional heterogeneity. Results: The architectural complexity of histological images has shown that single measurements are often insufficient. Inhomogeneity of distribution depends not only on percentage content of proliferation phase but also on how the phase fills the space. Lacunarity curves demonstrate variations in the sampled image sections. Since the spatial distribution of proliferation in each case is different, the width of the curves changes too. Image sections that have smaller numerical variations in the computed features correspond to neoplasms with spatially homogeneous proliferation, while larger variations correspond to cases where proliferation shows various degrees of clumping. Grade 1 (uniform/nonuniform: 74%/26%) and grade 3 (uniform: 100%) pNENs demonstrate a more homogeneous proliferation with grade 1 neoplasms being more variant, while grade 2 tumor regions render a more diverse landscape (50%/50%). Hence, some cases show an increased degree of spatial heterogeneity comparing to others with similar grade. Whether this is a sign of different tumor biology and an association with a more benign/malignant clinical course needs to be investigated further. The extent and range of spatial heterogeneity has the potential to be evaluated as a prognostic marker. Conclusions: The association with tumor grade as well as the rationale that the methodology reflects true tumor architecture supports the technical soundness of the method. This reflects a general approach which is relevant to other solid tumors and biomarkers. Drawing upon the merits of computational biomedicine, the approach uncovers salient features for use in future studies of clinical relevance.
60 APPLIED LIFE SCIENCES↗
Shape and texture
Methods used to measure the geometrical properties of regions in a segmented image are discussed including the use of centroids, moments, and principle axes. In addition, statistical picture properties, particularly those which describe visual texture, are discussed. Gray level statistics, local property statistics, and autocorrelation and power spectrum are addressed.
Rosenfeld, A.↗
Juxtatumoral perinephric fat analysis in clear cell renal cell carcinoma
Purpose: The purpose of the study was to evaluate the feasibility of using contrast-enhanced computed tomography (CECT)-based texture analysis (CTTA) metrics to differentiate between juxtatumoral perinephric fat (JPF) surrounding low-grade (ISUP 1–2) versus high-grade (ISUP 3–4) clear cell renal cell carcinoma (ccRCC). Methods: In this IRB-approved study, we retrospectively queried the surgical database between June 2009 and April 2016 and identified 83 patients with pathologically confirmed ccRCC (low grade: n = 54, mean age = 61.5 years, 18F/35M; high grade n = 30, mean age = 61.7 years, 8F/22M) who also had pre-operative multiphase CT acquisitions. CT images were transferred to a 3D workstation, and nephrographic phase JPF regions were manually segmented. Using an in-house developed Matlab program, a CTTA panel comprising of texture metrics extracted using six different methods, histogram, 2D- and 3D-Gray-level co-occurrence matrix (GLCM) and Gray-level difference matrix (GLDM), and 2D-Fast Fourier Transform (FFT) analyses, was applied to the segmented images to assess JPF textural heterogeneity in low- versus high-grade ccRCC. Univariate analysis and receiver-operator characteristics (ROC) analysis were used to assess interclass differences in texture metrics and their prediction accuracy, respectively. Results: All methods except GLCM consistently revealed increased heterogeneity in the JPF surrounding high- versus low-grade ccRCC. FFT showed increased complexity index (p < 0.01). Histogram analysis showed increased kurtosis and positive skewness in (p < 0.03), and GLDM analysis showed decreased measure of correlation coefficient (MCC) (p < 0.04). Several of the GLCM metrics showed statistically significant (p < 0.04) textural differences between the two groups, but with no consistent trend. ROC analysis showed that MCC in GLCM analysis had an area under the curve of 0.75. Conclusions: Our study suggests that CTTA of ccRCC shows statistically significant textural differences in JPF surrounding high- versus low-grade ccRCC.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Classifying Urban Land Covers Using Local Indices of Spatial Complexity
The skewed statistical distributions of land cover types in complex, heterogeneous urban areas limits the effectiveness of traditional spectrally based maximum-likelihood classifiers. This work examines the utility of fractal dimension and Moran's I index of spatial autocorrelation in segmenting high-resolution panchromatic and lower-resolution multispectral imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze multi-temporal and multi-platform imagery of Atlanta, Georgia. In this example, land cover change trajectories from forest or grassland to built up land covers lead to decreased spatial autocorrelation. In lower resolution imagery such as Landsat MSS, the complex details of forested land covers and urbanized areas are smoothed, and texture-based change detection is less effective. Although segmentation of panchromatic images is possible using fractal dimension or Moran's I, widely differing land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification.
Arumugam, Mahesh↗
SU-C-201-04: Quantification of Perfusion Heterogeneity Based On Texture Analysis for Fully Automatic Detection of Ischemic Deficits From Myocardial Perfusion Imaging
Purpose: Texture-based quantification of image heterogeneity has been a popular topic for imaging studies in recent years. As previous studies mainly focus on oncological applications, we report our recent efforts of applying such techniques on cardiac perfusion imaging. A fully automated procedure has been developed to perform texture analysis for measuring the image heterogeneity. Clinical data were used to evaluate the preliminary performance of such methods. Methods: Myocardial perfusion images of Thallium-201 scans were collected from 293 patients with suspected coronary artery disease. Each subject underwent a Tl-201 scan and a percutaneous coronary intervention (PCI) within three months. The PCI Result was used as the gold standard of coronary ischemia of more than 70% stenosis. Each Tl-201 scan was spatially normalized to an image template for fully automatic segmentation of the LV. The segmented voxel intensities were then carried into the texture analysis with our open-source software Chang Gung Image Texture Analysis toolbox (CGITA). To evaluate the clinical performance of the image heterogeneity for detecting the coronary stenosis, receiver operating characteristic (ROC) analysis was used to compute the overall accuracy, sensitivity and specificity as well as the area under curve (AUC). Those indices were compared to those obtained from the commercially available semi-automatic software QPS. Results: With the fully automatic procedure to quantify heterogeneity from Tl-201 scans, we were able to achieve a good discrimination with good accuracy (74%), sensitivity (73%), specificity (77%) and AUC of 0.82. Such performance is similar to those obtained from the semi-automatic QPS software that gives a sensitivity of 71% and specificity of 77%. Conclusion: Based on fully automatic procedures of data processing, our preliminary data indicate that the image heterogeneity of myocardial perfusion imaging can provide useful information for automatic determination of the myocardial ischemia.
60 APPLIED LIFE SCIENCES↗