Comparison of thresholding techniques for quantifying portland cement hydrates using synchrotron microtomography

Quantification of synthesized hydration products using synchrotron microtomography and spectral analysis

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In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ``fragmentation'' step allows one to find the elementary textures of the model, while the ``reconstruction'' step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

A new image segmentation algorithm based on Markov random field (MRF) and ant colony system (ACS) is presented in this paper. Information positive feedback and heuristic search, the characters of ACS, were applied for the image segmentations with MRF model. The maximum a posterior (MAP) global best solution of segmentations will be got though MRF, which describes image data relations by local correlations instead of global image possibility distributions. Compared with the simulated annealing (AS), ACS needs less time to search the global best solution. In this paper we proposed a segmentation algorithm combined MRF with ACS, which not only applied ACS as optimization algorithm but also introduced the neighborhood pheromone interaction rules into ACS under MRF model. Especially the pheromone interaction update provided remunerative information to ants in a neighborhood instead of an ant, which could accelerate the optimizing velocity and restrain the relative blur noise. The followed image segmentations experiments proved that this novel algorithm could reach a satisfied result among the noise restraint, edges preservation and computation complexity.

We demonstrate the utility of dendrograms at representing the essential features of the hierarchical structure of the isosurfaces for molecular line data cubes. The dendrogram of a data cube is an abstraction of the changing topology of the isosurfaces as a function of contour level. The ability to track hierarchical structure over a range of scales makes this analysis philosophically different from local segmentation algorithms like CLUMPFIND. Points in the dendrogram structure correspond to specific volumes in data cubes defined by their bounding isosurfaces. We further refine the technique by measuring the properties associated with each isosurface in the analysis allowing for a multiscale calculation of molecular gas properties. Using COMPLETE13CO (J = 1→ 0) data from the L1448 region in Perseus and mock observations of a simulated data cube, we identify regions that have a significant contribution by self-gravity to their energetics on a range of scales. We find evidence for self-gravitation on all spatial scales in L1448, although not in all regions. In the simulated observations, nearly all of the emission is found in objects that would be self-gravitating if gravity were included in the simulation. We reconstruct the size-line-width relationship within the data cube using the dendrogram-derived properties and find it follows the standard relation: σv ∝ R0.58. Finally, we show that constructing the dendrogram of CO (J = 1→ 0) emission from the Orion-Monoceros region allows for the identification of giant molecular clouds in a blended molecular line data set using only a physically motivated definition (self-gravitating clouds with masses >5 × 104 M☉).

A companion to the Insight Toolkit An introduction to the theory of modern medical image processing, including the analysis of data from - X-ray computer tomography, - magnetic resonance imaging, - nuclear medicine, - and ultrasound. Using an algorithmic approach, and providing the mathematical, statistical, or signal processing as needed for background, the authors describe the principles of all methods implemented in the Insight Toolkit (ITK), a freely available, open- source, object-oriented library. The emphasis is on providing intuitive descriptions of the principles and illustrative examples of results from the leading filtering, segmentation, and registration methods. This book covers the mathematical foundations of important techniques such as: - Statistical pattern recognition, - PDE-based nonlinear image filtering, - Markov random fields, - Level set methods, - Deformable models, - Mutual information, image-based registration - Non-rigid image data fusion With contributions from: Elsa Angelini, Brian Avants, Stephen Aylward, Ting Chen, Jeffrey Duda, Jim Gee, Luis Ibanez, Celina Imielinska, Yinpeng Jin, Jisung Kim, Bill Lorensen, Dimitris Metaxas, Lydia Ng, Punam Saha, George Stetten, Tessa Sundaram, Jay Udupa, Ross Whitaker, Terry Yoo, and Ying Zhuge. The Insight Toolkit is part of the Visible Human Project from the National Library of Medicine, with support from NIDCR, NINDS, NIMH, NEI, NSF, TATRC, NCI, and NIDCD.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.

Volumetric analysis of the brain from MR images is an important biomedical research tool. Segmentation of the brain parenchyma and its constituent tissue types, the gray matter and the white matter, is necessary for volumetric information in longitudinal and cross-sectional studies. We have implemented and compared two different classes of algorithms for segmentation of the brain parenchyma. In the first algorithm a combination of automatic thresholding and 3-D mathematical morphology was used to segment the brain while in the second algorithm an optical flow-based 3-D non-rigid registration approach was used to warp an MR head atlas to the subject brain. For tissue classification within the brain area a 3-D Markov Random Field model was used in conjunction with supervised and unsupervised classification. The algorithms described above were validated on a data set provided at the Internet Brain Segmentation Repository that consists of 20 normal T1 volumes (3 mm slice thickness) with manually segmented brain and manually classified tissues. While the morphological segmentation algorithm had an average similarity index of 0.918, the atlas-based brain segmentation algorithm has an average similarity index of 0.953. The supervised tissue classification had an average similarity index of 0.833 for gray matter voxels and 0.766 for white matter voxels. The performance of these algorithms is quite acceptable to end-users both in terms of accuracy and speed.

Dimensional X-ray Computed Tomography (CT) is a rapidly expanding field of research due to the numerous advantages this technique offers over conventional measurement technologies, most notably, the ability to measure internal features of a component. Tactile and optical Coordinate Measurement Machines (CMM), currently used in the manufacturing production industry, record points on the external surface of a workpiece by measuring the contact point of a physical probe or the reflection of projected light. X-ray CT has the ability to capture full volumetric data, since X-rays are transmitted through the entire object, revealing features which are otherwise invisible. Over the past five years, interest in this field has grown in the UK, with an increasing number of organisations in industry and research having access to X-ray CT machines and the wide range of manufacturers, offering new systems specifically designed for dimensional metrology applications.Despite this, the complexity of data acquisition required for dimensional measurement using X-ray CT has made it difficult to estimate the measurement uncertainty. This has hindered the generation of standards and full-scale adoption of this technique in industry. Due to the nature of X-ray imaging, a number of non-linear influence factors exist which have the potential to cause dimensional measurement error. These influences must be better understood to reduce and ideally, compensate error.In this doctoral thesis, the effects of the influence factors associated with CT data acquisition are studied, specifically, beam hardening and a finite X-ray source size. The effects these have on the quality of X-ray CT data are well understood; typically degrading the achievable contrast and spatial resolution of the CT image. However, the effects on dimensional measurement are less well understood due to the complexity of their interactions before reconstruction of the final image. These influences are modelled in a simulated CT acquisition to quantify any systematic effects on determination of edges in the CT image. The results are then validated by experimentally replicating the simulation set-up.In this work, it is found that beam hardening and a finite source diameter can lead to systematic errors in the edge position within the CT image. Beam hardening generally leads to dilation of the edge; where the edge position moves in the direction of the surface vector. In contrast, a finite source diameter can lead to erosion of the edge; where the edge position moves in an opposing direction to the surface vector.

The past 20 years has seen a progressive evolution of computer vision algorithms for unsupervised 2D image segmentation. While earlier efforts relied on Markov random fields and efficient optimization (graph cuts, etc.), the next wave of methods beginning in the early part of this century were, in the main, stovepiped. Of these 2D segmentation efforts, one of the most popular and, indeed, one that comes close to being a state of the art method is the ultrametric contour map (UCM). The pipelined methodology consists of (i) computing local, oriented responses, (ii) graph creation, (iii) eigenvector computation (globalization), (iv) integration of local and global information, (v) contour extraction, and (vi) superpixel hierarchy construction. UCM performs well on a range of 2D tasks. Consequently, it is somewhat surprising that no 3D version of UCM exists at the present time. To address that lack, we present a novel 3D supervoxel segmentation method, dubbed 3D UCM, which closely follows its 2D counterpart while adding 3D relevant features. The methodology, driven by supervoxel extraction, combines local and global gradient-based features together to first produce a low-level supervoxel graph. Subsequently, an agglomerative approach is used to group supervoxel structures into a segmentation hierarchy with explicitly imposed containment of lower-level supervoxels in higher-level supervoxels. Comparisons are conducted against state of the art 3D segmentation algorithms. The considered applications are 3D spatial and 2D spatiotemporal segmentation scenarios. For the latter comparisons, we present results of 3D UCM with and without optical flow video pre-processing. As expected, when motion correction beyond a certain range is required, we demonstrate that 3D UCM in conjunction with optical flow is a very useful addition to the pantheon of video segmentation methods.

Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images

A parallel image segmentation algorithm using relaxation with varying neighborhoods and its mapping to array processors

International audience