Application Of Texture Analysis Research Articles

Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging.

Introduction:Visual inspection by magnetic resonance (MR) images cannot detect microscopic tissue changes occurring in MS in normal appearing white matter (NAWM) and may be perceived by the human eye as having the same texture as normal white matter (NWM). The aim of the study was to evaluate computer aided diagnosis (CAD) system using texture analysis (TA) in MR images to improve accuracy in identification of subtle differences in brain tissue structure.Material and Methods:The MR image database comprised 50 MS patients and 50 healthy subjects. Up to 270 statistical texture features extract as descriptors for each region of interest. The feature reduction methods used were the Fisher method, the lowest probability of classification error and average correlation coefficients (POE+ACC) method and the fusion Fisher plus the POE+ACC (FFPA) to select the best, most effective features to differentiate between MS lesions, NWM and NAWM. The features parameters were used for texture analysis with principle component analysis (PCA) and linear discriminant analysis (LDA). Then first nearest-neighbour (1-NN) classifier was used for features resulting from PCA and LDA. Receiver operating characteristic (ROC) curve analysis was used to examine the performance of TA methods.Results:The highest performance for discrimination between MS lesions, NAWM and NWM was recorded for FFPA feature parameters using LDA; this method showed 100% sensitivity, specificity and accuracy and an area of Az = 1 under the ROC curve.Conclusion:TA is a reliable method with the potential for effective use in MR imaging for the diagnosis and prediction of MS.

Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis?

Several previous clinical or preclinical studies using computerized texture analysis of MR Images have demonstrated much more clinical discrimination than visual image analysis by the radiologist. In muscular dystrophy, a discriminating power has been already demonstrated with various methods of texture analysis of magnetic resonance images (MRI-TA). Unfortunately, a scale gap exists between the spatial resolutions of histological and MR images making a direct correlation impossible. Furthermore, the effect of the various histological modifications on the grey level of each pixel is complex and cannot be easily analyzed. Consequently, clinicians will not accept the use of MRI-TA in routine practice if TA remains a “black box” without clinical correspondence at a tissue level. A goal therefore of the multicenter European COST action MYO-MRI is to optimize MRI-TA methods in muscular dystrophy and to elucidate the histological meaning of MRI textures.

Application of texture analysis to muscle MRI: 2 – technical recommendations

A goal of the multicenter European Cooperation in Science and Technology (COST) action MYO-MRI is to optimize Magnetic Resonance Imaging Texture Analysis (MRI-TA) methods for application in the study of muscle disease. This paper deals with recommendations on the optimal methodology to collect the MRI data, to analyse it via texture analysis and to make conclusions from the resultant texture parameter data. A full and detailed description is provided with respect to MR image quality control, sequence choice, image pre-processing, region of interest selection, texture analysis methods and data analysis. A series of conclusions are presented.

Применение текстурного анализа для оценки стабильности распределения падения напряжения по длине крупногабаритного резистора из полимерных композиционных материалов

Применение текстурного анализа для оценки стабильности распределения падения напряжения по длине крупногабаритного резистора из полимерных композиционных материалов

Accurate fibre orientation measurement for carbon fibre surfaces

Carbon- and glass fibre materials exhibit challenging optical properties, in particular highly specular reflectivity. State of the art vision-based sensor systems use diffuse light setups to suppress specular reflections and apply texture analysis to obtain fibre orientations for surface patches.We investigate the reflection behaviour of fibrous materials. The derived reflection model is the basis for a sensor system that directly measures fibre orientation as well as diffuse and specular reflectivity per pixel. The proposed sensor is robust to changes of the fibre material and capable to deliver fast and accurate information about fibre direction without time consuming texture analysis. An empirical evaluation shows that the root mean squared error of calculated fibre angles is around or below 1°.

P16.02 * ADC TEXTURE - AN IMAGING BIOMARKER FOR HIGH GRADE GLIOMA?

INTRODUCTION: Median survival for high grade gliomas is limited, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion weighted MRI and its estimate apparent diffusion coefficient, ADC, has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of the present study was to investigate the potential of applying texture analysis in conjunction with multivariate ADC image for identifying early imaging biomarkers. MATERIALS AND METHODS: 23 consecutive high-grade glioma patients aged ≥18 years after diagnostic biopsy or resection were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. MRI assessment three months after completion of radio-chemotherapy was used for classifying tumor progression or regression. A baseline exam was performed within one week before treatment start, day one, two weeks and six weeks into the treatment. ADC maps and T1 weighted anatomical images with and without contrast enhancement were collected, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal and transversal planes, giving a total of 66 textural descriptors for each tumor. Principal component analysis, PCA, was applied to reduce dimensionality of the data, and the five largest components, or scores, were used in subsequent analyses. RESULTS: The score scatter plots revealed that the first, fourth and fifth components combined exhibited a pattern that strongly correlated to survival. Two groups could easily be identified: One with a median survival after diagnosis of 908 days and one with 299 days, p = 0.0005. MRI three months after completion of radiotherapy, age, surgical procedure and tumor grade all harbored prognostic information, but not to the same extent. CONCLUSION: By combining PCA and texture analysis ADC texture characteristics are identified, that seems to host pretreatment prognostic information, independent of known prognostic factors such as age, stage and surgical procedure. Validation of these data in a larger patient cohort is ongoing at our department.

926: Application of texture analysis to SPECT images of 125I-A5B7 anti-CEA antibody localisation in metastatic colorectal cancer models: Correlation with histological microarchitecture and response to antivascular therapy

926: Application of texture analysis to SPECT images of 125I-A5B7 anti-CEA antibody localisation in metastatic colorectal cancer models: Correlation with histological microarchitecture and response to antivascular therapy

Application of Texture Analysis for Optimizing Thermo-Mechanical Treatment of a High Mn TWIP Steel

A recently introduced processing route consisting of cold rolling and recovery annealing allows the production of TWIP steels with high yield strength along with appreciable uniform elongation due to the thermal stability of mechanically induced nanoscale twins. A wide range of strength-ductility combinations was obtained using recovery and recrystallization annealing of 30%, 40%, and 50% cold-rolled Fe-23Mn-1.5Al-0.3C TWIP steel. Texture measurement during cold rolling and annealing was proven to be a suitable tool to determine the optimal deformation degree and annealing time for this processing method. As a consequence, texture analysis can be used to predict the final materials properties.

Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging

The purpose of this study was to evaluate the potential utility of texture analysis of parametric apparent diffusion coefficient (ADC) maps in quantifying hepatic fibrosis. To this end, using ex vivo murine liver tissues from a dietary model of hepatic fibrosis, an array of texture analysis techniques, including histogram-based, gray-level co-occurrence matrix-based, and gray-level run-length-based features, was used to evaluate correlations with liver fibrosis. Moderate to very strong correlation between several of the texture-based features and both subjective as well as digital image analysis-based assessments of hepatic fibrosis was demonstrated. This rigorous study of texture analysis applied to parametric ADC maps in a liver fibrosis model study demonstrates and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis.

Automated classification of breast cancer morphology in histopathological images

Background The morphology of a breast cancer tumour, as examined through an optical microscope, is currently assessed visually by the pathologist in parallel with making the cancer diagnosis. The grade of differentiation, which describes how closely the morphology of the tumour resembles the corresponding healthy tissue of an organ, is undisputedly related to the outcome of breast cancer [1]. However, tumour grade is largely regarded as an unreliable prognostic factor due to its poor reproducibility [2]. The visually determined morphology is afflicted with a poor interand intra observer agreement, which prevents grade from being fully utilized as an important outcome predictor. The same pathologist may assign different grade to the same tumour when assessment is repeated, and different pathologists disagree to a substantial level when assessing the same tumour [3]. Computational diagnostic tools for estimating the morphological properties of cancer tissue would enable objective and reproducible alternative for diagnosis. This could be achieved by fully utilizing the recent advances in digital microscopy and computer vision [4,5]. Some attempts have already been made for automated grading of histopathological breast cancer images, but these studies have covered only limited amount of data or produce just a partial grading [6,7]. We propose a texture based algorithm for automated classification of breast cancer morphology. The method uses the recently introduced LPQ [8] as well as LBP [9] descriptors and an SVM classifier. The LPQ and LBP descriptors each form a histogram representing the statistical texture properties and have been used earlier in many texture analysis applications which include surface inspection [9], tissue analysis [5], and face recognition [8], whereas SVM represents the state of the art among supervised learning based classification algorithms.

Application of texture analysis in tear film surface assessment based on videokeratoscopy

PurposeVideokeratoscopy images can be used for the non-invasive assessment of the tear film. In this work the applicability of an image processing technique, textural-analysis, for the assessment of the tear film in Placido disc images has been investigated. MethodsIn the presence of tear film thinning/break-up, the reflected pattern from the videokeratoscope is disturbed in the region of tear film disruption. Thus, the Placido pattern carries information about the stability of the underlying tear film. By characterizing the pattern regularity, the tear film quality can be inferred. In this paper, a textural features approach is used to process the Placido images. This method provides a set of texture features from which an estimate of the tear film quality can be obtained. The method is tested for the detection of dry eye in a retrospective dataset from 34 subjects (22-normal and 12-dry eye), with measurements taken under suppressed blinking conditions. ResultsTo assess the capability of each texture-feature to discriminate dry eye from normal subjects, the receiver operating curve (ROC) was calculated and the area under the curve (AUC), specificity and sensitivity extracted. For the different features examined, the AUC value ranged from 0.77 to 0.82, while the sensitivity typically showed values above 0.9 and the specificity showed values around 0.6. Overall, the estimated ROCs indicate that the proposed technique provides good discrimination performance. ConclusionsTexture analysis of videokeratoscopy images is applicable to study tear film anomalies in dry eye subjects. The proposed technique appears to have demonstrated its clinical relevance and utility.

Texture Analysis of the Brain in Amyotrophic Lateral Sclerosis

In this paper, we applied texture analysis to evaluate cerebral degeneration in amyotrophic lateral sclerosis (ALS). Two well-known methods, the gray level co-occurrence matrix (GLCM) and the gray level aura matrix (GLAM) were employed to extract texture features from routine T1 and T2 MR images. Texture features were analyzed by statistical inference, support vector machine to determine classification rate, and receiver operator characteristic curve (ROC) analysis. Twenty control subjects (average age = 56.8± 12.4) and 19 ALS patients (average age = 56.7± 13.7) were recruited for the study. Texture features were statistically different in ALS compared to controls (p< 10−4 for T1, and p< 10−5 for T2) and provided a classification rate with more than 76%, and 82% accuracy on T1 and T2 weighted images, respectively. ROC analysis yielded area under the curves approaching 0.93, and a maximal sensitivity and specificity of 100% and 95%, respectively. Texture features moderately correlated with parenchymal brain volume suggesting that atrophy partially accounted for the texture results; however, texture features had a superior classification rate indicating that other cerebral pathology due to ALS besides atrophy was being captured by texture analysis. We conclude that texture analysis shows promise as a quantitative biomarker to study cerebral degeneration in ALS. To our best knowledge, this is the first study to apply the GLAM as a texture method to medical image analysis, showing that it provides features superior (or at least comparable) to the well known GLCM features.

Computer-Aided Diagnosis in Phase Contrast Imaging X-Ray Computed Tomography for Quantitative Characterization of ex vivo Human Patellar Cartilage

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Development of a structure analysis algorithm on structures from CuCl2⋅2H2O crystallization with agricultural products

Crystallization patterns emerge when an aqueous dihydrate Copper chloride (CuCl2⋅2H2O) solution in the presence of organic additives (juices/extracts) is crystallized on a glass plate. The emerging patterns are additive-specific and reflect physiological processes like maturation and ageing, the effect of processing, feeding regime and production system in a broad range of agricultural products. The patterns and their underlying structures are evaluated visually by means of defined morphological criteria and by means of computerized image analysis, respectively. The currently applied texture analysis algorithm reflects the spatial linear relationships between grey-scale values of the scanned crystallization structures, rendering the zero point arbitrary and constraining data analysis to the ordinal scale. Furthermore the algorithm is non-consistent with the physically defined geometric properties of the crystallization structures.In this article the development of a structure analysis algorithm is described and discussed which allows a quantification of the crystallization structures by computing 15 width-, and length-parameters, introducing a non-arbitrary zero-point and an equidistant scale which permits all statistical measures. The algorithm is applied to crystallization structures produced from carrot samples which shows it reflects the monotonic relation between physically defined geometric properties of the crystallization structures and laboratory procedure parameters influencing the overall morphological features of the crystallization structures. For instance the nucleation time, which is the time elapsed prior to initial nucleation of the crystallization structure, and the circular region of interest (ROI) around the geometric center of the glass plate used in image analysis evaluation. It is concluded that this structure analysis algorithm is a valuable addition to the image evaluation tools applicable for crystallization investigations of agricultural products, augmenting the image analysis with a non-arbitrary zero point and an equidistant scale which permits all statistical measures.

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

BackgroundTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical imagesMethodsImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.ResultsEarly evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.ConclusionThis review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.Teaching Points• Tumor spatial heterogeneity is an important prognostic factor.• Image texture analysis is an approach of quantifying heterogeneity.• Different methods can be applied, including statistical-, model-, and transform-based methods.• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

Application of Texture Analysis and Kohonen Map for Region Segmentation of Pavement Images for Crack Detection

The first phase of a research study on detecting cracks in pavements is described. For reliable crack detection, various regions in a road image have to be segmented accurately. A procedure based on the texture and color properties of different regions of images is used in conjunction with the Kohonen map, also known as the self-organizing map. Accuracy of 89.7% was obtained with classification based on the Kohonen map of images taken with a regular digital camera and simple lighting setup. Furthermore, a complementary algorithm is described to remove spurious classifications caused by inaccuracies in the trained Kohonen map. With the help of this algorithm, an overall segmentation accuracy of 97.7% is reported. This research is expected to affect other problems in transportation engineering, such as road boundary detection and road marking inspection. The detection of cracks from the segmented regions will be addressed in the future.

MRI texture analysis in multiple sclerosis.

Multiple sclerosis (MS) is a complicated disease characterized by heterogeneous pathology that varies across individuals. Accurate identification and quantification of pathological changes may facilitate a better understanding of disease pathogenesis and progression and help identify novel therapies for MS patients. Texture analysis evaluates interpixel relationships that generate characteristic organizational patterns in an image, many of which are beyond the ability of visual perception. Given its promise detecting subtle structural alterations texture analysis may be an attractive means to evaluate disease activity and evolution. It may also become a new tool to assess therapeutic efficacy if technique issues are resolved and pathological correlates are further confirmed. This paper describes the concept, strategies, and considerations of MRI texture analysis; summarizes applications of texture analysis in MS as a measure of tissue integrity and its clinical relevance; then discusses potentially future directions of texture analysis in MS.

Application of texture analysis to ventilation SPECT/CT data

It is demonstrated that textural parameters calculated from functional pulmonary CT data have the potential to provide a robust and objective quantitative characterisation of inhomogeneity in lung function and classification of lung diseases in routine clinical applications. Clear recommendations are made for optimum data preparation and textural parameter selection. A new set of platform-independent software tools are presented that are implemented as plug-ins for ImageJ. The tools allow segmentation and subsequent histogram-based and grey-level co-occurrence matrix based analysis of the regions of interest. The work-flow is optimised for use in a clinical environment for the analysis of transverse Computed Tomography (CT) scans and lung ventilation scans based on SPECT. Consistency tests are made against other texture analysis plug-ins and simulated lung CT data. The same methods are then applied to patient data consisting of a healthy reference group and one patient group each who suffered from asthma, chronic obstructive pulmonary disease (COPD), and COPD plus lung cancer. The potential for disease classification based on computer analysis is evaluated.

Texture Analysis: A Review of Neurologic MR Imaging Applications

Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.

Ultrasound image texture analysis of the intima and media layers of the common carotid artery and its correlation with age and gender

The intima–media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the composition and texture of the media layer (ML) can be used as an indicator for the risk of stroke. In this study, we investigate the application of texture analysis of the ML of the CCA and how texture is affected by age and gender. The study was performed on 100 longitudinal-section ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. The images were separated into three different age groups, namely below 50, 50–60, and above 60 years old. Furthermore, the images were separated according to gender. A total of 61 different texture features were extracted from the intima layer (IL), the ML, and the intima–media complex (IMC). The ML and the IMC were segmented manually by a neurovascular expert and also automatically by a snakes segmentation system. We have found that male patients tended to have larger media layer thickness (MLT) values as compared to the MLT of female patients of the same age. We have found significant differences among texture features extracted from the IL, ML and IMC from different age groups. Furthermore, for some texture features, we found that they follow trends that correlate with a patient's age. For example, the gray-scale median GSM of the ML falls linearly with increasing MLT and with increasing age. Our findings suggest that ultrasound image texture analysis of the media layer has potential as an assessment biomarker for the risk of stroke.