Two-dimensional Binary Images Research Articles

Chronic Undernutrition Differentially Changes Muscle Fiber Types Organization and Distribution in the EDL Muscle Fascicles.

Fiber type composition, organization, and distribution are key elements in muscle functioning. These properties can be modified by intrinsic and/or extrinsic factors, such as undernutrition and injuries. Currently, there is no methodology to quantitatively analyze such modifications. On one hand, we propose a fractal approach to determine fiber type organization, using the fractal correlation method in software Fractalyse. On the other hand, we applied the kernel methodology from machine learning to build radial-basis functions for the spatial distribution of fibers (distribution functions), by dividing into square cells a two-dimensional binary image for the spatial distribution of fibers from a muscle fascicle and mounting on each cell a radial-basis function in such a way that the sum of all cell functions creates a smooth version of the fiber histogram on the cell grid. The distribution functions thus created belong in a reproducing kernel Hilbert space which permits us to regard them as vectors and measure distances and angles between them. In the present study, we analyze fiber type organization and distribution in fascicles (F2, F3, F4, and F5) of the extensor digitorum longus muscle (EDLm) from control and undernourished male rats. Fibers were classified according to the ATPase activity in slow, intermediate, and fast. Then, (x, y) coordinates of fibers were used to build binary images and distribution functions for each fiber type and both conditions. The fractal organization analysis showed that fast and intermediate fibers, from both groups, had a fractal organization within the four fascicles, i.e., the fiber assembly is distributed in clusters. We also show that chronic undernutrition altered the organization of fast fibers in the F3, although it still is considered a fractal organization. Distribution function analysis showed that each fiber type (slow, intermediate, and fast) has a unique distribution within the fascicles, in both conditions. However, chronic undernutrition modified the intra-fascicular fiber type distributions, except in the F2. Altogether, these results showed that the methodology herein proposed allows for analyzing fiber type organization and distribution modifications. On the other side, we show that chronic undernutrition alters not only the fiber type composition but also the organization and distribution, which could affect the muscle functioning, and ultimately, its behavior (e.g., locomotion).

Characterizing the shape patterns of dimorphic yeast pseudohyphae.

Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.

CoGI: Towards Compressing Genomes as an Image.

Genomic science is now facing an explosive increase of data thanks to the fast development of sequencing technology. This situation poses serious challenges to genomic data storage and transferring. It is desirable to compress data to reduce storage and transferring cost, and thus to boost data distribution and utilization efficiency. Up to now, a number of algorithms / tools have been developed for compressing genomic sequences. Unlike the existing algorithms, most of which treat genomes as one-dimensional text strings and compress them based on dictionaries or probability models, this paper proposes a novel approach called CoGI (the abbreviation of Compressing Genomes as an Image) for genome compression, which transforms the genomic sequences to a two-dimensional binary image (or bitmap), then applies a rectangular partition coding algorithm to compress the binary image. CoGI can be used as either a reference-based compressor or a reference-free compressor. For the former, we develop two entropy-based algorithms to select a proper reference genome. Performance evaluation is conducted on various genomes. Experimental results show that the reference-based CoGI significantly outperforms two state-of-the-art reference-based genome compressors GReEn and RLZ-opt in both compression ratio and compression efficiency. It also achieves comparable compression ratio but two orders of magnitude higher compression efficiency in comparison with XM--one state-of-the-art reference-free genome compressor. Furthermore, our approach performs much better than Gzip--a general-purpose and widely-used compressor, in both compression speed and compression ratio. So, CoGI can serve as an effective and practical genome compressor. The source code and other related documents of CoGI are available at: http://admis.fudan.edu.cn/projects/cogi.htm.

Comparison of shapes of two-dimensional figures with the use of morphological spectra and EMD metrics

It is shown that the problems of real-time classification of two-dimensional figures and binary images can be successfully solved on the basis of comparison of discrete---continuous morphological pattern spectra with the use of the EMD-L1 metric.

Shape Fitting for the Shape Control System of Silicon Single Crystal Growth

Shape fitting, including straight line and ellipse fitting, plays an important role in the (cylinder-) shape control system of silicon single crystal growth, because the straight lines and ellipse in the crystal image contain the important horizontal circle center and diameter information. This information can be used as control variables so that the grown crystal approximates to a perfect cylinder, and thus can be used as high-quality source materials. In this paper, we develop new straight line and ellipse fitting algorithms. The key points are as follows. We formulate the two-dimensional (2-D) binary image into a single-snapshot array signal of a virtual sensor array, and casts the angle estimation problem of straight lines into the direction finding one of virtual incoming sources. Based on the virtual array manifold and potential incoming angles, the relevant over-complete dictionary is constructed, and thus a sparse regression problem is formed. To solve such a regression problem, we introduce the weight vector sparsity term into the conventional linear least-squares support vector regression framework to estimate the angles of these straight lines. Based on the estimated angles and potential offsets, another over-complete dictionary is constructed, and thus the image can be looked upon as the sparse representation of these dictionary atoms. Since the constructed dictionary is of the same size as the image, we use the compressed sensing theory to reduce the relevant dimensionality and then apply the aforementioned sparse regression method to obtain the relevant offsets of these straight lines. We derive a new second-order polynomial of ellipse equation to obtain the ellipse parameters to avoid the trival solution from the conventional polynomial model. Some simulation and experimental examples are given to illustrate the effectiveness of the proposed algorithms.

Accelerating computation of Euclidean distance map using the GPU with efficient memory access

Recent graphics processing units (GPUs), which have many processing units, can be used for general purpose parallel computation. To utilise the powerful computing ability, GPUs are widely used for general purpose processing. Since GPUs have very high memory bandwidth, the performance of GPUs greatly depends on memory access. The main contribution of this paper is to present a GPU implementation of computing Euclidean distance map (EDM) with efficient memory access. Given a two-dimensional (2D) binary image, EDM is a 2D array of the same size such that each element stores the Euclidean distance to the nearest black pixel. In the proposed GPU implementation, we have considered many programming issues of the GPU system such as coalesced access of global memory and shared memory bank conflicts, and so on. To be concrete, by transposing 2D arrays, which are temporal data stored in the global memory, with the shared memory, the main access from/to the global memory enables to be performed by coalesced access. In practice, we have implemented our parallel algorithm in the following three modern GPU systems: Tesla C1060, GTX 480 and GTX 580. The experimental results have shown that, for an input binary image with size of 9216 × 9216, our implementation can achieve a speedup factor of 54 over the sequential algorithm implementation.

Characterization of fat-connective tissue size distribution in pre-sliced pork hams using multifractal analysis

Fat-connective tissue size distribution (FSD) in hams is a fundamental physical property for its quality assessment. FSD is related to the sensory properties such as texture, taste, quality of raw meat and visual appearance. In this paper we present a tool to carry out the multifractal analysis (MFA) of two-dimensional binary images of pre-sliced pork hams through the calculation of the f( α)-spectra, Rényi ( D q ) dimensions, and associated statistical regressions and parameters. The application is presented for the structural characterization of FSD in three qualities of pork hams (high yield, medium yield and premium quality hams) using image sections of 512 × 512 pixels 2 with a spatial resolution of 0.102 mm/pixel. MFA was carried out using the method of moments in the optimized box size range of 32–512 pixels for all the ham images using powers of 2, and estimating the probability distribution for moments ranging from −10 < q < 10 in steps of 0.5. The experimental results suggest that MFA has a discriminating effect among the three types of ham using the maximum entropy ( H max ∗ ) and correlation dimension D 2. This investigation revealed the usefulness of the MFA dimensions as quantitative descriptors of texture analysis and pattern distributions of FSD in pre-sliced ham images.

Lamb wave characterization of corrosion-thinning in aircraft stringers: Experiment and three-dimensional simulation

The development of automatic guided wave interpretation for detecting corrosion in aluminum aircraft structural stringers is described. The dynamic wavelet fingerprint technique (DWFT) is used to render the guided wave mode information in two-dimensional binary images. Automatic algorithms then extract DWFT features that correspond to the distorted arrival times of the guided wave modes of interest, which give insight into changes of the structure in the propagation path. To better understand how the guided wave modes propagate through real structures, parallel-processing elastic wave simulations using the finite integration technique (EFIT) has been performed. Three-dimensional (3D) simulations are used to examine models too complex for analytical solutions. They produce informative visualizations of the guided wave modes in the structures and mimic the output from sensors placed in the simulation space. Using the previously developed mode extraction algorithms, the 3D EFIT results are compared directly to their experimental counterparts.

Lamb wave detection of limpet mines on ship hulls

This paper describes the use of ultrasonic guided waves for identifying the mass loading due to underwater limpet mines on ship hulls. The Dynamic Wavelet Fingerprint Technique (DFWT) is used to render the guided wave mode information in two-dimensional binary images because the waveform features of interest are too subtle to identify in time domain. The use of wavelets allows both time and scale features from the original signals to be retained, and image processing can be used to automatically extract features that correspond to the arrival times of the guided wave modes. For further understanding of how the guided wave modes propagate through the real structures, a parallel processing, 3D elastic wave simulation is developed using the finite integration technique (EFIT). This full field, technique models situations that are too complex for analytical solutions, such as built up 3D structures. The simulations have produced informative visualizations of the guided wave modes in the structures as well as mimicking directly the output from sensors placed in the simulation space for direct comparison to experiments. Results from both drydock and in-water experiments with dummy mines are also shown.

Optimizing two-pass connected-component labeling algorithms

We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.

An efficient [formula omitted] time 3D all nearest neighbor algorithm from image processing perspective

In this paper, we solve the k-dimensional all nearest neighbor ( kD_ANN) problem, where k = 2 or 3, on a linear array with a reconfigurable pipelined bus system (LARPBS) from image processing perspective. First, for a two-dimensional (2D) binary image of size N × N , we devise an algorithm for solving the 2D_ANN problem using a LARPBS of size N 2 + ϵ , where 0 < ϵ ⪡ 1 . Then, for a three-dimensional (3D) binary image of size N × N × N , we devise an algorithm for solving the 3D_ANN problem using a LARPBS of size N 3 + ɛ , where 0 < ɛ < 1 . To the best of our knowledge, all results derived above are the best O ( 1 ) time 2D_ANN and 3D_ANN algorithms on the LARPBS model known.

Optical Watermarking for Printed Document Authentication

This article describes a novel visual information concealment technique, referred to as optical watermarking, for the authentication of original printed documents. An optical watermark is a two-dimensional binary image. It can be of any shape and can be printed on any part of a document. The optical watermark is constructed by the superposition of multiple two-dimensional binary images (referred to as layers), each with different carrier structural patterns embedding various hidden information. The hidden information is embedded into each layer using phase modulation. Based on properties of the human visual system and modulation principle, the hidden information becomes visible to the human eyes only when a right "key" is positioned on top of the optical watermark with the right alignment. Here, "keys" play the similar role as keys in encryption, that is, to decode hidden information. Thus, with such a "lock and key" approach, it greatly improves the security level of the optical watermark. In addition, the multiple layer structure of the optical watermark makes it extremely robust against reverse engineering attacks. Due to its high security and tight link with electronic document systems, which requires documents to be finally printed on paper, the optical watermark has been applied to various electronic document systems. These are online ticketing, online bill of lading, and remote signing and printing of documents, where critical and unique information are embedded in watermarks and printed together with individual documents for future authentication. It has also been used in offline and traditional antiforgery applications, such as brand protection, preprinted high-value tickets, and identification documents

A program for fractal and multifractal analysis of two-dimensional binary images: Computer algorithms versus mathematical theory

In this paper we present a tool to carry out the multifractal analysis of binary, two-dimensional images through the calculation of the Rényi D( q) dimensions and associated statistical regressions. The estimation of a (mono)fractal dimension corresponds to the special case where the moment order is q = 0.

Gene Network Inference Using Optical Array Logic

In this paper, we propose a novel inference method for a gene network using optical array logic (OAL).1–3) OAL is utilized as a descriptive language for multipurpose parallel information processing on general electronic hardware. We develop an effective algorithm to extract direct regulations in a redundant gene network provided by biological experiments. In the proposed method, a gene interaction matrix is converted into a two-dimensional discrete binary image. The image is processed using parallel operations provided by OAL. After several steps of OAL operations indirect regulations in the gene network can be eliminated. The algorithm is believed to avoid undesired elimination of links caused by loop structures. In the simulation using artificial network data, the proposed technique achieves more than 90% sensitivity.

Stochastic simulations of remodeling applied to a two-dimensional trabecular bone structure

In this investigation the stochastic dynamics of trabecular bone remodeling (which is a key concept in a range of remodeling models) is verified. For this purpose, two-dimensional binary images of horizontal histological sections of lumbar vertebral bone (L-3) were collected from young and old subjects. Then the stochastic algorithm of remodeling was applied to the images of young trabecular bone. The resulting images of “simulated” old structures and those of “real” old structures were analyzed to evaluate the following architectural parameters: bone volume/trabecular volume; mean area of marrow cavities; mean trabeculae length; marrow space star volume; Euler number density; trabecular thickness; trabecular number; trabecular separation; mean two-point distance along the skeleton of trabecular structure; probability of disconnection; the ratio of marrow space star volume to geometrical volume and dimensionless ratio of mean area of marrow cavities; and mean trabecular length squared (form factor). Using the parametric t-test, the groups of simulated and real old bone images were compared. It is found that the p value of the t-test is never less than 0.20. For eight parameters the p value is >0.45. It is concluded that, as long as the horizontal sections of lumbar vertebrae are considered (sections perpendicular to the direction of the main spine load), the stochastic algorithm of bone remodeling will properly reproduce the architectural properties of trabecular bone.

Using three-dimensional reconstructed microstructures for predicting intrinsic permeability of reservoir rocks based on a Boolean lattice gas method

This paper presents a method for predicting the intrinsic permeability of porous media based on the integration of the local velocity field. Three-dimensional representations of the porous structure are reconstructed from two-dimensional binary images, after segmentation of digital images acquired from thin plates, commonly used in microscopy. Velocity field is calculated on these three-dimensional representations using a Boolean lattice gas method (LGA). Reconstruction is based on a Gaussian stochastic simulation. Mercury-intrusion results furnish auxiliary data that are used for the estimation of a critical percolation diameter and to establish a necessary condition for the binary source images to give accurate predictions of permeability, considering the intrinsic limitations of the reconstruction process. Reconstruction method and connection loss, resolution factor, adherence conditions and the effects of Boolean noise in the calculation of permeability are fully discussed. The method is used to simulate flows through several petroleum reservoir rocks, leading to intrinsic permeability prediction. Simulation is compared with experimental results. Considered as an intrinsic permeability prediction method based on the geometrical information that is possible to recovery from microscopy thin plates, three-dimensional reconstruction appears to be the most critical step in present simulation scheme.

Speeding up stochastic reconstructions of binary images from limited projection directions

In earlier work, a stochastic method for reconstructing certain classes of two-dimensional binary images from limited projection directions was presented. In the present study, we experiment with different implementations of this method to minimize the running time. Our fastest implementation is based on a look-up table and pre-generated arrays of random integers. This is more than 40 times faster than the implementation used in the earlier work. This speedup makes it practical to conduct extensive searches to find the optimal values of the method's parameters for each class of images to be reconstructed.

Stochastic Segmentation Using Gibbs Priors

In earlier work, a fast stochastic method for reconstructing a certain class of two-dimensional binary images from projections using Gibbs priors was presented. In the present study, we introduce a stochastic segmentation of magnetic resonance gray-scale images of trabecular bone using Gibbs priors. We show some results as well as some post-processing that can be used to clean up segmentations of noisy gray-scale images.

Linear-time recognition of connectivity of binary images on 1-bit inter-cell communication cellular automaton

We introduce a new class of two-dimensional cellular automata (CAs) whose inter-cell communication is restricted to 1-bit and propose a linear-time connectivity recognition algorithm for two-dimensional binary images. Precisely, it is shown that a set of two-dimensional connected binary images of size m× n can be recognized in 2( m+ n)+ O(1) steps by a two-dimensional CA with 1-bit inter-cell communication.

A Study of Two-Dimensional Binary Images of Wear Debris as an Indicator of Distinct Wear Conditions

Feasibility of the observation of two-dimensional binary images of wear debris for detecting the change of wear conditions is studied. Identification and comparison of wear debris are quantitatively carried out using numerical parameters and statistical analysis. Wear tests are run to produce various kinds of wear debris. The images of wear debris from wear tests are captured with image acquisition equipment. By thresholding, two-dimensional binary images of wear debris are made and, then, morphological parameters are used to quantify the images of debris. Analysis of variance is applied to determine which morphological parameters are significantly affected by the difference in wear conditions. It is shown that some morphological parameters of two-dimensional binary images of wear debris can statistically indicate the distinction of wear conditions.