Measurement of intracellular strain on deformable substrates with texture correlation.

Mechanical stimuli are important factors that regulate cell proliferation, survival, metabolism and motility in a variety of cell types. The relationship between mechanical deformation of the extracellular matrix and intracellular deformation of cellular sub-regions and organelles has not been fully elucidated, but may provide new insight into the mechanisms involved in transducing mechanical stimuli to biological responses. In this study, a novel fluorescence microscopy and image analysis method was applied to examine the hypothesis that mechanical strains are fully transferred from a planar, deformable substrate to cytoplasmic and intranuclear regions within attached cells. Intracellular strains were measured in cells derived from the anulus fibrosus of the intervertebral disc when attached to an elastic silicone membrane that was subjected to tensile stretch. Measurements indicated cytoplasmic strains were similar to those of the underlying substrate, with a strain transfer ratio (STR) of 0.79. In contrast, nuclear strains were much smaller than those of the substrate, with an STR of 0.17. These findings are consistent with previous studies indicating nuclear stiffness is significantly greater than cytoplasmic stiffness, as measured using other methods. This study provides a novel method for the study of cellular mechanics, including a new technique for measuring intranuclear deformations, with evidence of differential magnitudes and patterns of strain transferred from the substrate to cell cytoplasm and nucleus.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

An FPGA Based Coprocessor for GLCM and Haralick Texture Features and Their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

Image-Based Spatialization

An approach to spatialization is described in which the pixels of an image determine both spatial and other attributes of individual elements in a multi-channel musical texture. The application of this technique in the author’s composition Spaced Images with Noise and Lines is discussed in detail. The relationship of this technique to existing image-to-sound mappings is discussed. The particular advantage of modifying spatial properties with image filters is considered.

PatchCity: Procedural City Generation using Texture Synthesis

PatchCity is a new approach to the procedural generation of city models. The algorithm uses texture synthesis to create a city layout in the visual style of one or more input examples. Data is provided in vector graphic form from either real or synthetic city definitions. The paper describes the PatchCity algorithm, illustrates its use, and identifies its strengths and limitations. The technique provides a greater range of features and styles of city layout than existing generative methods, thereby achieving results that are more realistic. An open source implementation of the algorithm is available.

Applicabilité de la texture couleur à la différentiation des classes d’occupation du territoire sur des images satellitales multispectrales

La texture est un élément clé pour l’interprétation des images de télédétection à fine résolution spatiale. L’intégration de l’information texturale dans un processus de classification automatisée des images se fait habituellement via des images de texture, souvent créées par le calcul de matrices de co-occurrences (MCO) des niveaux de gris. Une MCO est un histogramme des fréquences d’occurrence des paires de valeurs de pixels présentes dans les fenêtres locales, associées à tous les pixels de l’image utilisée; une paire de pixels étant définie selon un pas et une orientation donnés. Les MCO permettent le calcul de plus d’une dizaine de paramètres décrivant, de diverses manières, la distribution des fréquences, créant ainsi autant d’images texturales distinctes. L’approche de mesure des textures par MCO a été appliquée principalement sur des images de télédétection monochromes (ex. images panchromatiques, images radar monofréquence et monopolarisation). En imagerie multispectrale, une unique bande spectrale, parmi celles disponibles, est habituellement choisie pour générer des images de texture. La question que nous avons posée dans cette recherche concerne justement cette utilisation restreinte de l’information texturale dans le cas des images multispectrales. En fait, l’effet visuel d’une texture est créé, non seulement par l’agencement particulier d’objets/pixels de brillance différente, mais aussi de couleur différente. Plusieurs façons sont proposées dans la littérature pour introduire cette idée de la texture à plusieurs dimensions. Parmi celles-ci, deux en particulier nous ont intéressés dans cette recherche. La première façon fait appel aux MCO calculées bande par bande spectrale et la seconde utilise les MCO généralisées impliquant deux bandes spectrales à la fois. Dans ce dernier cas, le procédé consiste en le calcul des fréquences d’occurrence des paires de valeurs dans deux bandes spectrales différentes. Cela permet, en un seul traitement, la prise en compte dans une large mesure de la « couleur » des éléments de texture. Ces deux approches font partie des techniques dites intégratives. Pour les distinguer, nous les avons appelées dans cet ouvrage respectivement « textures grises » et « textures couleurs ». Notre recherche se présente donc comme une analyse comparative des possibilités offertes par l’application de ces deux types de signatures texturales dans le cas spécifique d’une cartographie automatisée des occupations de sol à partir d’une image multispectrale. Une signature texturale d’un objet ou d’une classe d’objets, par analogie aux signatures spectrales, est constituée d’une série de paramètres de texture mesurés sur une bande spectrale à la fois (textures grises) ou une paire de bandes spectrales à la fois (textures couleurs). Cette recherche visait non seulement à comparer les deux approches intégratives, mais aussi à identifier la composition des signatures texturales des classes d’occupation du sol favorisant leur différentiation : type de paramètres de texture / taille de la fenêtre de calcul / bandes spectrales ou combinaisons de bandes spectrales. Pour ce faire, nous avons choisi un site à l’intérieur du territoire de la Communauté Métropolitaine de Montréal (Longueuil) composé d’une mosaïque d’occupations du sol, caractéristique d’une zone semi urbaine (résidentiel, industriel/commercial, boisés, agriculture, plans d’eau…). Une image du satellite SPOT-5 (4 bandes spectrales) de 10 m de résolution spatiale a été utilisée dans cette recherche. Puisqu’une infinité d’images de texture peuvent être créées en faisant varier les paramètres de calcul des MCO et afin de mieux circonscrire notre problème nous avons décidé, en tenant compte des études publiées dans ce domaine : a) de faire varier la fenêtre de calcul de 3*3 pixels à 21*21 pixels tout en fixant le pas et l’orientation pour former les paires de pixels à (1,1), c'est-à-dire à un pas d’un pixel et une orientation de 135°; b) de limiter les analyses des MCO à huit paramètres de texture (contraste, corrélation, écart-type, énergie, entropie, homogénéité, moyenne, probabilité maximale), qui sont tous calculables par la méthode rapide de Unser, une approximation des matrices de co-occurrences, c) de former les deux signatures texturales par le même nombre d’éléments choisis d’après une analyse de la séparabilité (distance de Bhattacharya) des classes d’occupation du sol; et d) d’analyser les résultats de classification (matrices de confusion, exactitudes, coefficients Kappa) par maximum de vraisemblance pour conclure sur le potentiel des deux approches intégratives; les classes d’occupation du sol à reconnaître étaient : résidentielle basse et haute densité, commerciale/industrielle, agricole, boisés, surfaces gazonnées (incluant les golfs) et plans d’eau. Nos principales conclusions sont les suivantes a) à l’exception de la probabilité maximale, tous les autres paramètres de texture sont utiles dans la formation des signatures texturales; moyenne et écart type sont les plus utiles dans la formation des textures grises tandis que contraste et corrélation, dans le cas des textures couleurs, b) l’exactitude globale de la classification atteint un score acceptable (85%) seulement dans le cas des signatures texturales couleurs; c’est une amélioration importante par rapport aux classifications basées uniquement sur les signatures spectrales des classes d’occupation du sol dont le score est souvent situé aux alentours de 75%; ce score est atteint avec des fenêtres de calcul aux alentours de11*11 à 15*15 pixels; c) Les signatures texturales couleurs offrant des scores supérieurs à ceux obtenus avec les signatures grises de 5% à 10%; et ce avec des petites fenêtres de calcul (5*5, 7*7 et occasionnellement 9*9) d) Pour plusieurs classes d’occupation du sol prises individuellement, l’exactitude dépasse les 90% pour les deux types de signatures texturales; e) une seule classe est mieux séparable du reste par les textures grises, celle de l’agricole; f) les classes créant beaucoup de confusions, ce qui explique en grande partie le score global de la classification de 85%, sont les deux classes du résidentiel (haute et basse densité). En conclusion, nous pouvons dire que l’approche intégrative par textures couleurs d’une image multispectrale de 10 m de résolution spatiale offre un plus grand potentiel pour la cartographie des occupations du sol que l’approche intégrative par textures grises. Pour plusieurs classes d’occupations du sol un gain appréciable en temps de calcul des paramètres de texture peut être obtenu par l’utilisation des petites fenêtres de traitement. Des améliorations importantes sont escomptées pour atteindre des exactitudes de classification de 90% et plus par l’utilisation des fenêtres de calcul de taille variable adaptées à chaque type d’occupation du sol. Une méthode de classification hiérarchique pourrait être alors utilisée afin de séparer les classes recherchées une à la fois par rapport au reste au lieu d’une classification globale où l’intégration des paramètres calculés avec des fenêtres de taille variable conduirait inévitablement à des confusions entre classes.

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

Finding Texture Boundaries in Images

Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], together with their attributes, such as orientation, elongation, and intensity. Psychophysical studies suggest that texture boundaries are perceived where distributions of attributes over neighborhoods of textons differ significantly. However, these studies, which deal with synthetic images, neglect to consider two important questions: How can these textons be extracted from images of natural scenes? And how, exactly, are texture boundaries then found? This thesis proposes answers to these questions by presenting an algorithm for computing blobs from natural images and a statistic for measuring the difference between two sample distributions of blob attributes. As part of the blob detection algorithm, methods for estimating image noise are presented, which are applicable to edge detection as well.

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot