Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Chaotic synchronization in general network topology for scene segmentation

Chaotic synchronization has been discovered to be an important property of neural activities, which in turn has encouraged many researchers to develop chaotic neural networks for scene and data analysis. In this paper, we study the synchronization role of coupled chaotic oscillators in networks of general topology. Specifically, a rigorous proof is presented to show that a large number of oscillators with arbitrary geometrical connections can be synchronized by providing a sufficiently strong coupling strength. Moreover, the results presented in this paper not only are valid to a wide class of chaotic oscillators, but also cover the parameter mismatch case. Finally, we show how the obtained result can be applied to construct an oscillatory network for scene segmentation.

Chaotic synchronization in 2D lattice for scene segmentation

Synchronization and chaos play important roles in neural activities and have been applied in oscillatory correlation modeling for scene and data analysis. Although it is an extensively studied topic, there are still few results regarding synchrony in locally coupled systems. In this paper we give a rigorous proof to show that large numbers of coupled chaotic oscillators with parameter mismatch in a 2D lattice can be synchronized by providing a sufficiently large coupling strength. We demonstrate how the obtained result can be applied to construct an oscillatory network for scene segmentation. (C) 2007 Elsevier B.V. All rights reserved.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants. (C) 2008 Elsevier Inc. All rights reserved.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.

The Sphene-Centered Ocellar Texture: An Effect of Grain-Supported Flow and Melt Migration in a Hyperdense Magma Mush

The sphene-centered ocellar texture consists of leucocratic ocelli with sphene (titanite) crystals at the center, enclosed in a biotite-rich matrix. This texture has been recognized worldwide in hybrid intermediate rocks. On the basis of structural, petrological, and geochronological data from selected outcrops of the Variscan Ribadelago pluton (NW Iberian Massif), we propose that the ocelli were formed by migration and accumulation of a residual melt through a plagioclase- and biotite-dominated crystalline framework. At the late stage of crystallization, the magma acted as a hyperdense suspension and reacted to the pressure gradient caused by the regional stress field, entering the domain of grain-supported flow. Microstructures reveal that aligned crystal domains arose in the crystal framework from the shearing and compaction of the crystal mush and behaved as magmatic microshears. Relative displacement of adjacent crystal clusters along these microshears corresponded to the onset of Reynolds dilatancy that generated an expansion of the crystal mush, involving melt migration and pore aperture. The mineralogy of the ocelli, dominated by andesine and sphene, represents the composition of the migrating melt. The chemistry of this late, Ti-rich melt stems from the incongruent melting of biotite. Magmatic sphene from the ocelli yields a U-Pb age of 317 +/- 1 Ma, which represents the final crystallization of the hybridized magmatic system. Moreover, this texture offers an opportunity to better understand the rheological behavior of highly crystallized magmas.

Quartz recrystallization regimes, c-axis texture transitions and fluid inclusion reequilibration in a prograde greenschist to amphibolite facies mylonite zone (Ribeira Shear Zone, SE Brazil)

The crystal-plastic behavior of quartz mylonites from the Ribeira Shear Zone (SE Brazil), a major strike-slip structure that was active during a prograde metamorphic phase related to the Neoproterozoic Brasiliano-Pan African Orogeny, was investigated using a multi-method approach. Geothermobarometry results indicate deformational conditions ranging from similar to 300 to similar to 630 degrees C and 500-700 MPa. A strong correlation between mapped metamorphic zones and a dominance of different dynamic recrystallization mechanisms of quartz occurs within the mylonite zone. Bulging recrystallization (BLG) dominates within the chlorite zone between 300 and 410 degrees C, subgrain rotation recrystallization (SGR) operates within the biotite zone from 410 to 520 degrees C, and grain boundary migration recrystallization (GBM) dominates in the garnet zone above 520 degrees C. The development of quartz c-axis textures is mainly governed by temperature and dynamic recrystallization mechanisms. Textures from BLG zone mylonites are characterized by maxima around Z; SGR zone mylonites display single girdles or asymmetric type I crossed girdles; and GBM zone mylonites comprise maxima around Y and intermediate between X and Z. The scarcity or absence of water-bearing fluid inclusions in quartz mylonites from the SGR and GBM zones, which are dominated by carbonic inclusions, suggests water-deficient conditions, whereas BLG zone mylonites are dominated by water-bearing inclusions. This evidence indicates that water was available in the protoliths but has been eliminated with increasing deformation and deformation temperature. No effect of the water content variation on the quartz microstructural and recrystallized grain size evolution was detected, and little influence on c-axis texture development was observed. Most of the fluid inclusion densities were reequilibrated during the shear zone exhumation history, recording a decompression in the range of 300-500 MPa, while microstructural reequilibration effects related to the prograde metamorphism are largely preserved. Fluid inclusion microstructures and densities from two SGR zone samples preserved evidence for a near isothermal compression within the interior of the Ribeira Shear Zone during the prograde metamorphism. (C) 2009 Elsevier B.V. All rights reserved.