19 research outputs found
Textural Analysis of a Fibronectin Network During Early Embryogenesis
Fibronectin is a major extracellular matrix molecular component that plays a critical role in embryonic cell motility and morphogenesis. During early development, the fibronectin molecular network is nearly ubiquitous in distribution across the entire embryonic volume. As a result, the embryonic fibronectin distribution is functionally relevant to both cell motility and organogenesis. Despite its biological importance, the structural attributes of embryonic fibronectin distribution are poorly understood. The textural features of an extracellular matrix network like that of fibronectin is not known. By marking the embryonic tissue, obtained during specific stages of development, using fibronectin indirect immunofluorescence, fluorescent microscopy images capturing the embryonic fibronectin distribution were obtained. Using the properties of the gray scale co-occurrence matrix, textural attributes of a fibronectin network was derived for quail embryos during their early stages of morphogenesis. As a result, textural properties like inertia, correlation, uniformity, entropy and homogeneity were assessed for the medial (including the embryonic anteroposterior axis) and lateral (excluding the embryonic anteroposterior axis) fibronectin . The results not only demonstrated a noticeable heterogeneity of fibronectin textural properties across the embryonic regions examined, but also across the developmental stages that were studied. The spatial anisotropy and the temporal evolution across the developmental span in the textural attributes of embryonic fibronectin may have functional consequences for cell motility and morphogenesis. With this thesis, textural analysis has found yet another application, viz. the study of the surface structural attributes of an extracellular matrix protein network in the context of developmental biology. The work presented in this thesis is the first of its kind in applying texture analysis with gray scale co-occurrence matrix method to study the spatial distribution of fibronectin matrix within a given stage of embryonic development and the temporal textural changes as the embryonic development progresses from stage 5 through stage 9
A Vegetation Analysis on Horn Island, Mississippi, ca. 1940 Using Characteristic Dimensions Derived from Historical Aerial Photography
Horn Island is part of the MS/AL barrier island chain in the northern Gulf of Mexico located approximately 18kn off the coast of Mississippi. This island’s habitats have undergone many transitions over the last several decades. The goal of this study was to quantify habitat change over a seventy year period using historical black and white photography from 1940. Using present NAIP imagery from the USDA, habitat structure was estimated by using geo-statistics, and second order statistics, from a co-occurrence matrix, to characterize texture for habitat classification. Percent land cover was then calculated to determine overall land cover change over a seventy year period. The geostatistic of the horizontal spectral variation (CV) of image textures was used to estimate habitat structure using a multi scale approach if any characteristics of habitat texture could be delineated from CV histograms. The classification met with a result of an 80% habitat map of Horn Island ca. 1940, at 21x21 window size proving, that CV can be used successfully to classify text of historical black and white imagery. It was, also proven that CV can be used to characterize relative patch size for slash pine woodland habitat types, but not for habitats with smaller horizontal variations (i.e., marsh, and dune herbland)
Conceptual design study for an advanced cab and visual system, volume 2
The performance, design, construction and testing requirements are defined for developing an advanced cab and visual system. The rotorcraft system integration simulator is composed of the advanced cab and visual system and the rotorcraft system motion generator, and is part of an existing simulation facility. User's applications for the simulator include rotorcraft design development, product improvement, threat assessment, and accident investigation
IMPLEMENTATION OF IMAGE TEXTURE ANALYSIS USING NEIGHBORHOOD GREY TONE DIFFERENT MATRIX METHOD
Texturehas found in wide application in imageprocessing and it is commonly agreed
that texture analysis plays a fundamental role in classifying objects or images. Two
approaches have evolved over the years for texture analysis, which are called
statistical and structural analysis. In recent studies, the texture classification and
discrimination usually approached by using statistical analysis. Therefore, this project
is carried out to distinguish the application of statistical analysis especially in the
approach of Neighborhood Grey Tone Different Matrix (NGDTM) in image
classification. This project presents the NGTDMapproach of image texture analysis
by using MATLAB. Thus, three types of texture selected, which consist of thirty
images respectively, are analyzed using the algorithm of first and second order
statistic developed in MATLAB. First order statistic extractsthe statistical parameters
from the image and second order statistic specifically NGTDM emphasizes on the
intensities of neighboring pixels. The first and second order characteristics of each
image are varying to each other, which are lead to the classification of texture. As a
result, each of the input images is successfully classified regarding to its type of
texture. Future work on image classification can be conducted for the use of
application, such as medical image processing and remote sensing community
Mètode d'extracció multiparamètrica de caracterÃstiques de textura orientat a la segmentació d'imatges
Tal com es veurà en el següent capÃtol d'antecedents, existeixen formes molt variades d'afrontar l'anà lisi de textures però cap d'elles està orientada al cà lcul en temps real (video rate). Degut a la manca de mètodes que posin tant d'èmfasi en el temps de processat, l'objectiu d'aquesta tesi és definir i desenvolupar un nou mètode d'extracció de caracterÃstiques de textura que treballi en temps real. Per aconseguir aquesta alta velocitat d'operació, un altre objectiu és presentar el disseny d'una arquitectura especÃfica per implementar l'algorisme de cà lcul dels parà metres de textura definits, aixà com també l'algorisme de classificació dels parà metres i la segmentació de la imatge en regions de textura semblant.En el capÃtol 2 s'expliquen els diversos mètodes més rellevants dins la caracterització de textures. Es veuran els mètodes més importants tant pel que fa als enfocaments estadÃstics com als estructurals. També en el mateix capÃtol se situa el nou mètode presentat en aquesta tesi dins els diferents enfocaments principals que existeixen. De la mateixa manera es fa una breu ressenya a la sÃntesi de textures, una manera d'avaluar quantitativament la caracterització de la textura d'una imatge. Ens centrarem principalment, en el capÃtol 3, en l'explicació del mètode presentat en aquest treball: s'introduiran els parà metres de textura proposats, la seva necessitat i definicions. Al ser parà metres altament perceptius i no seguir cap model matemà tic, en aquest mateix capÃtol s'utilitza una tècnica estadÃstica anomenada anà lisi discriminant per demostrar que tots els parà metres introdueixen suficient informació per a la separabilitat de regions de textura i veure que tots ells són necessaris en la discriminació de les textures.Dins el capÃtol 4 veurem com es tracta la informació subministrada pel sistema d'extracció de caracterÃstiques per tal de classificar les dades i segmentar la imatge en funció de les seves textures. L'etapa de reconeixement de patrons es durà a terme en dues fases: aprenentatge i treball. També es presenta un estudi comparatiu entre diversos mètodes de classificació de textures i el mètode presentat en aquesta tesi; en ell es veu la bona funcionalitat del mètode en un temps de cà lcul realment reduït. S'acaba el capÃtol amb una anà lisi de la robustesa del mètode introduint imatges amb diferents nivells de soroll aleatori. En el capÃtol 5 es presentaran els resultats obtinguts mitjançant l'extracció de caracterÃstiques de textura a partir de diverses aplicacions reals. S'aplica el nostre mètode en aplicacions d'imatges aèries i en entorns agrÃcoles i sobre situacions que requereixen el processament en temps real com són la segmentació d'imatges de carreteres i una aplicació industrial d'inspecció i control de qualitat en l'estampació de teixits. Al final del capÃtol fem unes consideracions sobre dos efectes que poden influenciar en l'obtenció correcta dels resultats: zoom i canvis de perspectiva en les imatges de textura.En el capÃtol 6 es mostrarà l'arquitectura que s'ha dissenyat expressament per al cà lcul dels parà metres de textura en temps real. Dins el capÃtol es presentarà l'algorisme per a l'assignació de grups de textura i es demostrarà la seva velocitat d'operació a video rate.Finalment, en el capÃtol 7 es presentaran les conclusions i les lÃnies de treball futures que es deriven d'aquesta tesi, aixà com els articles que hem publicat en relació a aquest treball i a l'anà lisi de textures. Les referències bibliogrà fiques i els apèndixs conclouen el treball
IMPLEMENTATION OF IMAGE TEXTURE ANALYSIS USING NEIGHBORHOOD GREY TONE DIFFERENT MATRIX METHOD
Texturehas found in wide application in imageprocessing and it is commonly agreed
that texture analysis plays a fundamental role in classifying objects or images. Two
approaches have evolved over the years for texture analysis, which are called
statistical and structural analysis. In recent studies, the texture classification and
discrimination usually approached by using statistical analysis. Therefore, this project
is carried out to distinguish the application of statistical analysis especially in the
approach of Neighborhood Grey Tone Different Matrix (NGDTM) in image
classification. This project presents the NGTDMapproach of image texture analysis
by using MATLAB. Thus, three types of texture selected, which consist of thirty
images respectively, are analyzed using the algorithm of first and second order
statistic developed in MATLAB. First order statistic extractsthe statistical parameters
from the image and second order statistic specifically NGTDM emphasizes on the
intensities of neighboring pixels. The first and second order characteristics of each
image are varying to each other, which are lead to the classification of texture. As a
result, each of the input images is successfully classified regarding to its type of
texture. Future work on image classification can be conducted for the use of
application, such as medical image processing and remote sensing community
A content-based image retrieval system for texture and color queries
Cataloged from PDF version of article.In recent years, very large collections of images and videos have grown rapidly.
In parallel with this growth, content-based retrieval and querying the indexed collections
are required to access visual information. Two of the main components of
the visual information are texture and color. In this thesis, a content-based image
retrieval system is presented that computes texture and color similarity among
images. The underlying technique is based on the adaptation of a statistical approach
to texture analysis. An optimal set of five second-order texture statistics
are extracted from the Spatial Grey Level Dependency Matrix of each image, so
as to render the feature vector for each image maximally informative, and yet
to obtain a low vector dimensionality for efficiency in computation. The method
for color analysis is the color histograms, and the information captured within
histograms is extracted after a pre-processing phase that performs color transformation,
quantization, and filtering. The features thus extracted and stored within
feature vectors are later compared with an intersection-based method. The system
is also extended for pre-processing images to segment regions with different
textural quality, rather than operating globally over the whole image. The system
also includes a framework for object-based color and texture querying, which
might be useful for reducing the similarity error while comparing rectangular regions
as objects. It is shown through experimental results and precision-recall
analysis that the content-based retrieval system is effective in terms of retrieval
and scalability.Konak, Eyüp SabriM.S
Texture Analysis of Diffraction Enhanced Synchrotron Images of Trabecular Bone at the Wrist
The purpose of this study is to determine the correlation between texture features of Di raction
Enhanced Imaging (DEI) images and trabecular properties of human wrist bone in the assessment
of osteoporosis. Osteoporosis is a metabolic bone disorder that is characterized by reduced bone
mass and a deterioration of bone structure which results in an increased fracture risk. Since the
disease is preventable, diagnostic techniques are of major importance. Bone micro-architecture and
Bone mineral density (BMD) are two main factors related to osteoporotic fractures. Trabecular
properties like bone volume (BV), trabecular number (Tb.N), trabecular thickness (Tb.Th), bone
surface (BS), and other properties of bone, characterizes the bone architecture. Currently, however,
BMD is the only measurement carried out to assess osteoporosis. Researchers suggest that bone
micro-architecture and texture analysis of bone images along with BMD can provide more accuracy
in the assessment.
We have applied texture analysis on DEI images and extracted texture features. In our study,
we used fractal analysis, gray level co-occurrence matrix (GLCM), texture feature coding method
(TFCM), and local binary patterns (LBP) as texture analysis methods to extract texture features.
3D Micro-CT trabecular properties were extracted using SkyScanTM CTAN software. Then, we
determined the correlation between texture features and trabecular properties. GLCM energy fea-
ture of DEI images explained more than 39% of variance in bone surface by volume ratio (BS/BV),
38% of variance in percent bone volume (BV/TV), and 37% of variance in trabecular number
(Tb.N). TFCM homogeneity feature of DEI images explained more than 42% of variance in bone
surface (BS) parameter. LBP operator - LBP 11 of DEI images explained more than 34% of vari-
ance in bone surface (BS) and 30% of variance in bone surface density (BS/TV). Fractal dimension
parameter of DEI images explained more than 47% of variance in bone surface (BS) and 32% of
variance in bone volume (BV). This study will facilitate in the quanti cation of osteoporosis beyond
conventional BMD
A Stochastic Modeling Approach to Region-and Edge-Based Image Segmentation
The purpose of image segmentation is to isolate objects in a scene from the background. This is a very important step in any computer vision system since various tasks, such as shape analysis and object recognition, require accurate image segmentation. Image segmentation can also produce tremendous data reduction. Edge-based and region-based segmentation have been examined and two new algorithms based on recent results in random field theory have been developed. The edge-based segmentation algorithm uses the pixel gray level intensity information to allocate object boundaries in two stages: edge enhancement, followed by edge linking. Edge enhancement is accomplished by maximum energy filters used in one-dimensional bandlimited signal analysis. The issue of optimum filter spatial support is analyzed for ideal edge models. Edge linking is performed by quantitative sequential search using the Stack algorithm. Two probabilistic search metrics are introduced and their optimality is proven and demonstrated on test as well as real scenes. Compared to other methods, this algorithm is shown to produce more accurate allocation of object boundaries. Region-based segmentation was modeled as a MAP estimation problem in which the actual (unknown) objects were estimated from the observed (known) image by a recursive classification algorithms. The observed image was modeled by an Autoregressive (AR) model whose parameters were estimated locally, and a Gibbs-Markov random field (GMRF) model was used to model the unknown scene. A computational study was conducted on images having various types of texture images. The issues of parameter estimation, neighborhood selection, and model orders were examined. It is concluded that the MAP approach for region segmentation generally works well on images having a large content of microtextures which can be properly modeled by both AR and GMRF models. On these texture images, second order AR and GMRF models were shown to be adequate