Artificial Images Research Articles

An edge-based active contour model using an inflation/deflation force with a damping coefficient

This publication presents an edge-based active contour model using the inflation/deflation force, allowing active contour nodes to be moved to find object boundaries in a digital image. The methods proposed in this study make it possible to keep a high value of the inflation/deflation force for each node until the node approaches the boundary of the analysed shape. After the boundary searched for is reached, the value of the inflation/deflation force for these nodes is automatically damped. The solutions used in this paper are of major practical significance if the analysed images contain weak boundaries and/or strong noise at the same time, and on top of that there are strictures of the shape which should be approximated. Experiments were carried out for artificial images as well as USG and MRI medical images, and have confirmed the suitability of the solutions used.

Correlation analysis of spatio-temporal images for estimating two-dimensional flow velocity field in a rotating flow condition

Summary Flow velocity estimation in actual rivers using image processing technique has been highlighted for hydrometric communities in the last decades, and this technique is called Large Scale Particle Image Velocimetry (LSPIV). Although LSPIV has been successfully tested in many flow conditions, it has addressed several limitations estimating mean flow field because of difficult flow conditions such as rotating, lack of light and seeds, and noisy flow conditions. Recently, an alternative technique named STIV to use spatio-temporal images based on successively recorded images has been introduced to overcome the limitations of LSPIV. The STIV was successfully applied to obtain one-dimensional flow component in the river for estimating streamflow discharge, where the main flow direction is known. Using the 5th order of central difference scheme, the STIV directly calculated the mean angle of slopes which appeared as strips in the spatio-temporal images and has been proved to be more reliable and efficient for the discharge estimation as compared with the conventional LSPIV. However, yet it has not been sufficiently qualified to derive two-dimensional flow field in the complex flow, such as rotating or locally unsteady flow conditions. We deemed that it was because the strips in the given spatio-temporal images from not properly oriented for main flow direction are not narrow enough or clearly visible, thus the direct estimating strip slope could give erroneous results. Thereby, the STIV has been mainly applied for obtaining one-dimensional flow component. In this regard, we proposed an alternative algorithm to estimate the mean slope angle for enhancing the capability of the STIV, which used correlation coefficient between odd and even image splits from the given spatio-temporal image. This method was named CASTI (Correlation Analysis of Spatio-Temporal Image). This paper described the step-by-step procedure of the CASTI and validated its capability for estimating two-dimensional flow field in the rotating flow. For this purpose, we generated artificial images for lid-driven cavity flow where true velocity field can be manipulated and validated with respect to the proposed CASTI algorithm. As a result, the CASTI was successfully performed to capture two-dimensional velocity vector in the given cavity flow.

Characterizing Compactness of Geometrical Clusters Using Fuzzy Measures

Certain tasks in image processing require the preservation of fine image details, while applying a broad operation to the image, such as image reduction, filtering, or smoothing. In such cases, the objects of interest are typically represented by small, spatially cohesive clusters of pixels which are to be preserved or removed, depending on the requirements. When images are corrupted by the noise or contain intensity variations generated by imaging sensors, identification of these clusters within the intensity space is problematic as they are corrupted by outliers. This paper presents a novel approach to accounting for spatial organization of the pixels and to measuring the compactness of pixel clusters based on the construction of fuzzy measures with specific properties: monotonicity with respect to the cluster size; invariance with respect to translation, reflection, and rotation; and discrimination between pixel sets of fixed cardinality with different spatial arrangements. We present construction methods based on Sugeno-type fuzzy measures, minimum spanning trees, and fuzzy measure decomposition. We demonstrate their application to generating fuzzy measures on real and artificial images.

A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering

Determining the number of clusters, which is usually approved by domain experts or evaluated by clustering validity indexes, is an important issue in clustering analysis. This study discusses the effectiveness of clustering validity indexes for centroid-based partitional clustering algorithms. Most general-purpose clustering validity indexes take the minimum/maximum distance between a pair of data objects, a pair of cluster centroids, or an object and a centroid as an important evaluation factor; however, they may present unstable results, especially when two centroids are allocated closely. To alleviate this problem, a new clustering validity index, which is termed the Wu-and-Li index (WLI), is proposed in this paper. Our proposed WLI partially allows, to some extent, the existence of closely allocated centroids in the clustering results by considering not only the minimum but the median distances between a pair of centroids as well; therefore possessing better stability. The performances of WLI and some existing clustering validity indexes are evaluated and compared by running the fuzzy c-means algorithm for clustering various types of datasets, including artificial datasets, UCI datasets, and images. Experimental results have shown that WLI has the more accurate and satisfactory performance than other indexes.

A DENOISING OF BIOMEDICAL IMAGES

Abstract. Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).

Automatic Evaluation of Collagen Fiber Directions from Polarized Light Microscopy Images.

Mechanical properties of the arterial wall depend largely on orientation and density of collagen fiber bundles. Several methods have been developed for observation of collagen orientation and density; the most frequently applied collagen-specific manual approach is based on polarized light (PL). However, it is very time consuming and the results are operator dependent. We have proposed a new automated method for evaluation of collagen fiber direction from two-dimensional polarized light microscopy images (2D PLM). The algorithm has been verified against artificial images and validated against manual measurements. Finally the collagen content has been estimated. The proposed algorithm was capable of estimating orientation of some 35 k points in 15 min when applied to aortic tissue and over 500 k points in 35 min for Achilles tendon. The average angular disagreement between each operator and the algorithm was -9.3±8.6° and -3.8±8.6° in the case of aortic tissue and -1.6±6.4° and 2.6±7.8° for Achilles tendon. Estimated mean collagen content was 30.3±5.8% and 94.3±2.7% for aortic media and Achilles tendon, respectively. The proposed automated approach is operator independent and several orders faster than manual measurements and therefore has the potential to replace manual measurements of collagen orientation via PLM.

Unbiased-average minimum biased diffusion speckle denoising approach for synthetic aperture radar images

Means of synthetic aperture radar (SAR) images represent the radiation densities of scenes, and the preservation of means is significant in speckle denoising for the application of SAR images. We provide an improved scheme of the minimum biased diffusion (MinBAD) algorithm for speckle denoising using partial differential equations. Considering the characteristics of SAR speckle and the radiation accuracy for postprocessing needs, several improvements such as normalization, homomorphic transformation, and average-preserving processing are introduced into the MinBAD algorithm. Besides the equivalent number of looks and edge preserving index, a new index, radiation accuracy error, is defined to evaluate the denoising effect. Experimental results for both artificial images and real SAR images are used to validate the performance of the proposed unbiased-average MinBAD speckle reducing approach.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

Abstract. To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2’s continuous and manifold data stream.

Image Segmentation Using Parametric Contours With Free Endpoints.

In this paper, we introduce a novel approach for active contours with free endpoints. A scheme for image segmentation is presented based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.

Fast unsupervised texture segmentation using Texel similarity map

Unsupervised texture segmentation is a challenging topic in computer vision. It is difficult to obtain boundaries of texture regions automatically in real-time, especially for cluttered images. This paper presents a new fast unsupervised texture segmentation method. First, the Texel similarity map (TSM) is used to compare the changes of intensity and gray level of neighboring pixels to determine whether they are identical. Then, a scheme called multiple directions integral images (MDII) is proposed to quickly evaluate the TSM. With the aid of MDII, one pixel’s similarity value can be computed in constant time. Finally, the proposed segmentation method is tested on both artificial texture and natural images. Experimental results show that the proposed method performs well on a wide range of images, and outperforms state-of-the-art method on segmentation speed.

Improved Fuzzy C-Means Algorithm for Image Segmentation

In order to preserve more image details and enhance its robustness to noise for image segmentation, an improved fuzzy c-means algorithm (FCM) for image segmentation is presented by incorporating the local spatial information and gray level information in this paper. The modified membership function and clustering center function are more mathematically reasonable than those of the FLICM, so the iterative sequence can converge to a local minimum value of the improved objective function. The new fuzzy factor grants the algorithm a novel balance between robustness to noise and effectiveness of preserving the details. The revised algorithm flow has significantly accelerated the processing procedure. Through these improvements, the experiments on the artificial and real images show that the proposed algorithm is very effective.

Low-end mass function of the arches cluster

The initial mass function (IMF) of the Arches cluster, which was formed a few million years ago in the harsh environment of the Galactic center (GC), has long been a target of interest to those who study the GC and the theory of star formation. The distinct star-forming conditions in the GC might have caused the cluster to have a shallower slope or an elevated lower mass cutoff in its IMF. But its mass function has been revealed only down to 1-2 Msun (the lower limit of resolved stars), and the low- end mass function of the Arches is still unknown. To estimate the unresolved part of the Arches mass function, we have devised a novel photometric method that involves the histogram of pixel intensities in the observed image, which contains information on the unresolved, faint stars. By comparing the pixel intensity histograms (PIHs) of numerous artificial images constructed from model IMFs with the observed PIH, we find that the best-fit model IMF for the Arches cluster has a cutoff mass less than or similar to 0.1 Msun and a shape very close to that of the Kroupa MF. Our findings imply that the IMF of the Arches cluster is similar to those found in the Galactic disk.

Investigating carbon materials nanostructure using image orientation statistics

A new characterization method of the lattice fringe images of turbostratic carbons is proposed. This method is based on the computation of their orientation field without explicit detection of fringes. It allows meaningful insights into the material nanostructure and nanotexture at several scales, either qualitatively or quantitatively. The calculation of pairwise spatial statistics of the orientation field at short distance provides measurements of the coherence lengths along any direction, in particular along and orthogonally to the layers. These statistics also allow representing orientation coherence patterns typical of the observed nanostructure. At larger distances, the mean disorientation of the fringes is computed and information about the homogeneity of the sample is obtained. An experimental validation is carried out on various artificial images and an application to the characterization of four bulk turbostratic carbons is provided.

Modified genetic algorithm-based sub-pixel mapping

Sub-pixel mapping (SPM) is a promising way to predict the location of endmembers within mixed pixels at the sub pixel level. SPSAM is a real time processing algorithm for SPM, but it will generate a lot of isolated sub pixels because land cover classes were assigned to sub pixels in order. In this letter, a novel method is proposed to realize SPM. It contains two main steps: sub-pixel/pixel spatial attraction model (SPSAM) is first used to generate the initial SPM result; and then modified genetic algorithm (MGA) is applied as the post-processing method to obtain more accurate results. Three different sets of data are used for experiments: simple artificial images and two sets of real remote sensing images. The results show MGA/SPSAM outperforms the conventional SPM methods.

A novel image thresholding algorithm based on neutrosophic similarity score

Image thresholding is an important field in image processing. It has been employed to segment the images and extract objects. A variety of algorithms have been proposed in this field. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a new general formal framework to study the neutralities’ origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing and computer vision research fields. This paper proposed a novel algorithm based on neutrosophic similarity score to perform thresholding on image. We utilize the neutrosophic set in image processing field and define a new concept for image thresholding. At first, an image is represented in the neutrosophic set domain via three membership subsets T, I and F. Then, a neutrosophic similarity score (NSS) is defined and employed to measure the degree to the ideal object. Finally, an optimized value is selected on the NSS to complete the image thresholding task. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method’s performance. The experimental results demonstrate that the proposed method selects the threshold values effectively and properly. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision.

Red blood cell cluster separation from digital images for use in sickle cell disease.

The study of cell morphology is an important aspect of the diagnosis of some diseases, such as sickle cell disease, because red blood cell deformation is caused by these diseases. Due to the elongated shape of the erythrocyte, ellipse adjustment and concave point detection are applied widely to images of peripheral blood samples, including during the detection of cells that are partially occluded in the clusters generated by the sample preparation process. In the present study, we propose a method for the analysis of the shape of erythrocytes in peripheral blood smear samples of sickle cell disease, which uses ellipse adjustments and a new algorithm for detecting notable points. Furthermore, we apply a set of constraints that allow the elimination of significant image preprocessing steps proposed in previous studies. We used three types of images to validate our method: artificial images, which were automatically generated in a random manner using a computer code; real images from peripheral blood smear sample images that contained normal and elongated erythrocytes; and synthetic images generated from real isolated cells. Using the proposed method, the efficiency of detecting the two types of objects in the three image types exceeded 99.00%, 98.00%, and 99.35%, respectively. These efficiency levels were superior to the results obtained with previously proposed methods using the same database, which is available at http://erythrocytesidb.uib.es/. This method can be extended to clusters of several cells and it requires no user inputs.

A novel image segmentation algorithm based on neutrosophic similarity clustering

Segmentation is an important research area in image processing, which has been used to extract objects in images. A variety of algorithms have been proposed in this area. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a general formal framework to study the neutralities’ origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing algorithms. This paper proposed a novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images. We utilize the neutrosophic set in image processing field and define a new similarity function for clustering. At first, an image is represented in the neutrosophic set domain via three membership sets: T, I and F. Then, a neutrosophic similarity function (NSF) is defined and employed in the objective function of the clustering analysis. Finally, the new defined clustering algorithm classifies the pixels on the image into different groups. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method's performance. The experimental results demonstrate that the NSC method segment the images effectively and accurately. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision.

Parametric polytope reconstruction, an application to crystal shape estimation.

In situ imaging techniques are a promising direction for monitoring the distribution of crystal sizes and shapes during a crystallization process. Nevertheless, no tractable method yet exists for estimating complex crystal shapes. In this paper, an in situ imaging setup is presented and a novel algorithm for crystal shape estimation from a pair of images is presented. It is shown that such a shape estimation problem can be turned into parametric polytope reconstruction from projections. Based on results in polyhedral geometry, it is demonstrated that an accurate estimate of the crystal shape can be computed by solving a nonlinear least-squares problem built from samples in images and a prior model of the crystal. Effectiveness of the approach is proven on artificial and real images. Results show that very accurate estimations of crystal shapes can be obtained from well-chosen data points sampled on images.

REMOVAL OF IMPULSE BURSTS IN SATELLITE IMAGES

Characteristics of impulse bursts in satellite images are analyzed and methods for burst removal are consid- ered. Artificial compact burst model is proposed and test images are created. An advanced multipass algorithm for the detection and removal of compact bursts in the presence of both additive and multiplicative noise is proposed. The efficiency of the algorithm is evaluated quantitatively using the artificial test images and visually using the artificial test images and real radar and optical satellite images. It is shown through experiments that the proposed method removes impulse bursts efficiently while preserving information.

Bayesian scale space analysis of temporal changes in satellite images

We consider the detection of land cover changes using pairs of Landsat ETM+ satellite images. The images consist of eight spectral bands and to simplify the multidimensional change detection task, the image pair is first transformed to a one-dimensional image. When the transformation is non-linear, the true change in the images may be masked by complex noise. For example, when changes in the Normalized Difference Vegetation Index is considered, the variance of noise may not be constant over the image and methods based on image thresholding can be ineffective. To facilitate detection of change in such situations, we propose an approach that uses Bayesian statistical modeling and simulation-based inference. In order to detect both large and small scale changes, our method uses a scale space approach that employs multi-level smoothing. We demonstrate the technique using artificial test images and two pairs of real Landsat ETM+satellite images.