Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks

Dynamic texture is a recent field of investigation that has received growing attention from computer vision community in the last years. These patterns are moving texture in which the concept of selfsimilarity for static textures is extended to the spatiotemporal domain. In this paper, we propose a novel approach for dynamic texture representation, that can be used for both texture analysis and segmentation. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes of the video in order to combine appearance and motion features. We validate our method on three applications of dynamic texture that present interesting challenges: recognition, clustering and segmentation. Experimental results on these applications indicate that the proposed method improves the dynamic texture representation compared to the state of the art.

Applicazione di tecniche di analisi statistica e texture analysis all'imaging di risonanza magnetica di pazienti affetti da glioblastoma multiforme

Il seguente lavoro di tesi si è concentrato sull'analisi statistica dei dati prodotti dall'imaging di risonanza magnetica di pazienti affetti da tumori di alto grado, in particolare glioblastoma multiforme. Le tipologie di acquisizione d'immagine utilizzate sono state l'imaging pesato in T1 e il Diffusion-Weighted Imaging (DWI). Lo studio è stato suddiviso in due fasi: nella prima è stato considerato un campione di pazienti affetti da glioblastoma multiforme che, dopo il trattamento, avessero manifestato una ricaduta della malattia; per questi pazienti è stato quantificato in che modo la dose erogata durante la terapia si sia distribuita sul target del trattamento, in particolare nella porzione di tessuto in cui andrà a svilupparsi la recidiva. Nella seconda fase, è stato selezionato un campione più ristretto che disponesse, per entrambe le modalità di imaging, di un'acquisizione pre-terapia e di un numero sufficiente di esami di follow up; questo al fine di seguire retrospettivamente l'evoluzione della patologia e analizzare tramite metodi statistici provenienti anche dalla texture analysis, i dati estratti dalle regioni tumorali. Entrambe le operazioni sono state svolte tramite la realizzazione di software dedicati, scritti in linguaggio Matlab. Nel primo capitolo vengono fornite le informazioni di base relative ai tumori cerebrali, con un'attenzione particolare al glioblastoma multiforme e alle sue modalità di trattamento. Nel secondo capitolo viene fatta una panoramica della fisica dell'imaging di risonanza magnetica e delle tecniche di formazione delle immagini, con un'ampia sezione è dedicata all'approfondimento dell'imaging in diffusione. Nel terzo capitolo viene descritto il progetto, i campioni e gli strumenti statistici e di texture analysis utilizzati in questo studio. Il quarto capitolo è dedicato alla descrizione puntuale dei software realizzati durante questo lavoro e nel quinto vengono mostrati i risultati ottenuti dall'applicazione di questi ultimi ai campioni di pazienti esaminati.

Feasibility of texture analysis for the assessment of biochemical changes in meniscal tissue on T1 maps calculated from delayed gadolinium-enhanced magnetic resonance imaging of cartilage data: comparison with conventional relaxation time measurements

To (1) establish the feasibility of texture analysis for the in vivo assessment of biochemical changes in meniscal tissue on delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), and (2) compare textural with conventional T1 relaxation time measurements calculated from dGEMRIC data ("T1(Gd) relaxation times").

MRI texture analysis as means for addressing rehydration and milk diffusion of cereals

Cereals microstructure is one of the primary quality attributes of cereals. Cereals rehydration and milk diffusion depends on such microstructure and thus, the crispiness and the texture, which will make it more palatable for the final consumer. Magnetic Resonance Imaging (MRI) is a very powerful topographic tool since acquisition parameter leads to a wide possibility for identifying textures, structures and liquids mobility. It is suited for non-invasive imaging of water and fats. Rehydration and diffusion cereals processes were measured by MRI at different times and using two different kinds of milk, varying their fat level. Several images were obtained. A combination of textural analysis (based on the analysis of histograms) and segmentation methods (in order to understand the rehydration level of each variety of cereals) were performed. According to the rehydration level, no advisable clustering behavior was found. Nevertheless, some differences were noticeable between the coating, the type of milk and the variety of cereals

Snakes based tracking and texture analysis of microscopic images

Comunicación presentada en el VII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, SNRFAI, Barcelona, abril 1997.

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

Texture analysis and cartographic feature extraction /

"January 1985."

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

Comparison of Wavelet Transform and Time-Domain Analysis of Second Trimester Uterine Artery Doppler Waveforms in Screening for Pre-Eclampsia

The aim of this study was to compare time-domain waveform analysis of second-trimester uterine artery Doppler using the resistance index (RI) with waveform analysis using a mathematical tool known as wavelet transform for the prediction of pre-eclampsia (PE). This was a retrospective, nested case-cohort study of 336 women, 37 of whom subsequently developed PE. Uterine artery Doppler waveforms were analysed using both RI and waveform analysis. The utility of these indices in screening for PE was then evaluated using receiver operating characteristic curves. There were significant differences in uterine artery RI between the PE women and those with normal pregnancy outcome. After wavelet analysis, significant difference in the mean amplitude in wavelet frequency band 4 was noted between the 2 groups. The sensitivity for both Doppler RI and frequency band 4 for the detection of PE at a 10% false-positive rate was 45%. This small study demonstrates the application of wavelet transform analysis of uterine artery Doppler waveforms in screening for PE. Further prospective studies are needed in order to clearly define if this analytical approach to waveform analysis may have the potential to improve the detection of PE by uterine artery Doppler screening.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA)

OBJECTIVE Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS). METHODS We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald's criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons. RESULTS Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues. CONCLUSION DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.

Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging

Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions () were prestratified into enhancing lesions with increased permeability (EL+; ) and enhancing lesions with subtle permeability (EL−; ). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals. Results. Significant differences () were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow. Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.

Evolution of microstructure and texture in friction stir processed Al-Mg-Mn alloy

Friction stir processing was carried out on the Al-Mg-Mn alloy to achieve ultrafine grained microstructure. The evolution of microstructure and micro-texture was studied in different regions of the deformed sample, namely nugget zone, thermo-mechanically affected zone (TMAZ) and base metal. The average grain sizes of the nugget zone, TMAZ and base metal are 1.5 mu m +/- 0.5 mu m, 15 mu m +/- 8 mu m, and 80 mu m +/- 10 mu m, respectively. The TMAZ exhibits excessive deformation banding structure and sub-grain formation. The orientation gradient within the sub-grain is dependent on grain size, orientation, and distance from nugget zone. The microstructure was partitioned based on the grain orientation spread and grain size values to separate the recrystallized fraction from the deformed region in order to understand the micromechanism of grain refinement. The texture of both deformed and recrystallized regions are similar in nature. Microstructure and texture analysis suggest that the restoration processes are different in different regions of the processed sample. The transition region between nugget zone and TMAZ exhibits large elongated grains surrounded by fine equiaxed grains of different orientation which indicate the process of discontinuous dynamic recrystallization. Within the nugget zone, similar texture between deformed and recrystallized grain fraction suggests that the restoration mechanism is a continuous process.