Number Of Segmented Regions Research Articles

Morphological-based Analyses for Parameterizing Symmetry in Radio Galaxies

The morphology of radio galaxies can provide significant clues to describe the formation and evolution of galaxies in the Universe. Here, we aim to extract the morphological parameters of radio galaxies and define symmetry criteria as some of the essential factors of their shape explanations. We employed 67 radio galaxies, which include Fanaroff–Riley type 1 and type 2 galaxies, and their radio images from the FIRST and LoTSS surveys. We developed an automatic segmentation process to extract morphological properties such as the size of objects, eccentricity, and orientation of segmented regions from data sets. Using a maximum likelihood estimator, we show that the distributions of sizes follow a power-law function with exponents of −0.39 ± 0.06 and −0.55 ± 0.05 for the FIRST and LoTSS data, respectively. We found that type 2 radio galaxies have slightly lower eccentricities than type 1. We studied the relationships between size, eccentricity, and redshift in scatter plots. The size of galaxies (kpc2) demonstrates gently growing trends with increasing eccentricity in their scatter plots. We discussed the possible effect of the redshifts of the galaxies on this result. Depending on the number of segmented regions, we defined symmetry criteria based on proximity to the center of a galaxy in the optical band, eccentricity, orientation, and the quarter (q) of appearance in the image. We found that the mean symmetry obtained for two segmented regions that mainly emerged in two quarters via the condition of has a higher value than those obtained for other cases.

Automated noninvasive epithelial cell counting in phase contrast microscopy images with automated parameter selection.

SummaryCell counting is commonly used to determine proliferation rates in cell cultures and for adherent cells it is often a ‘destructive’ process requiring disruption of the cell monolayer resulting in the inability to follow cell growth longitudinally. This process is time consuming and utilises significant resource. In this study a relatively inexpensive, rapid and widely applicable phase contrast microscopy‐based technique has been developed that emulates the contrast changes taking place when bright field microscope images of epithelial cell cultures are defocused. Processing of the resulting images produces an image that can be segmented using a global threshold; the number of cells is then deduced from the number of segmented regions and these cell counts can be used to generate growth curves. The parameters of this method were tuned using the discrete mereotopological relations between ground truth and processed images. Cell count accuracy was improved using linear discriminant analysis to identify spurious noise regions for removal.The proposed cell counting technique was validated by comparing the results with a manual count of cells in images, and subsequently applied to generate growth curves for oral keratinocyte cultures supplemented with a range of concentrations of foetal calf serum. The approach developed has broad applicability and utility for researchers with standard laboratory imaging equipment.

Research on Image Segmentation based on Clustering Algorithm

Hierarchical clustering (HC) algorithm can obtain good clustering results, but it needs large storage and computational complexity for large image processing. Anew color image segmentation algorithm based on mean shift and hierarchical clustering algorithm named MSHC is presented in this paper. MSHC algorithm preprocesses an input image by MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of image. The number of segmented regions, instead of the number of image pixels, is considered as the input data scale of HC algorithm. The proximity between each cluster is calculated to form the proximity matrix, and then ward algorithm is employed to obtain the final segmentation results. MSHC algorithm is employed on color image and medical image segmentation.

Four color theorem and convex relaxation for image segmentation with any number of regions

Image segmentation is an essential problem in imaging science. One of the most successful segmentation models is the piecewise constant Mumford-Shah minimization model. This minimization problem is however difficult to carry out, mainly due to the non-convexity of the energy. Recent advances based on convex relaxation methods are capable of estimating almost perfectly the geometry of the regions to be segmented when the mean intensity and the number of segmented regions are known a priori. The next important challenge is to provide a tight approximation of the optimal geometry, mean intensity and the number of regions simultaneously while keeping the computational time and memory usage reasonable. In this work, we propose a new algorithm that combines convex relaxation methods with the four color theorem to deal with the unsupervised segmentation problem. More precisely, the proposed algorithm can segment any a priori unknown number of regions with only four intensity functions and four indicator (``labeling') functions. The number of regions in our segmentation model is decided by one parameter that controls the regularization strength of the geometry, i.e., the total length of the boundary of all the regions. The segmented image function can take as many constant values as needed.

Analysis of Radially Restored Images for Spherical Single Lens Cellphone Camera

A spherical single lens imaging system for cellphone camera is proposed in this research. The overall thickness from the front surface of the lens to the image sensor is approximately 4.5 mm, which is competitive compared to 6~7 mm of current cellphone camera modules that use compound lens system. One defect of this spherical single lens that deteriorates image quality is radially variant blur, which is produced by one of the optical aberrations called field curvature. We describe a novel method of blur restoration that employs polar image and polar Point Spread Function (PSF) converted from Cartesian image and Cartesian PSF. The blurred image was radially segmented into several regions, to which polar PSFs at different field angles were applied. Results of visual and quantitative evaluation indicate that number of segmented regions and weighted average of β/ n ratios, i.e., ratio of bandwidth to the number Circulant Blocks (CBs) of the polar PSF Block-Toeplitz-with-Circulant-Blocks (BTCB) matrices affect the restored image quality and Mean Square Error (MSE). Evaluation results on restored images using different polar image resolutions revealed that high resolution obtains good pixel continuity, whereas low resolution results in radially expanding pixel vacancies.

Adaptive fuzzy-K-means clustering algorithm for image segmentation

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Adaptive Fuzzy-K-means (AFKM) clustering for image segmentation which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using different consumer electronic products namely, for example, the common digital cameras and CCD cameras. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms. Both qualitative and quantitative analyses favour the proposed AFKM algorithm in terms of providing a better segmentation performance for various types of images and various number of segmented regions. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.

Suitable segmentation methodology based on pixel similarities for landmine detection in IR images

Identification of masked objects especially in detection of landmines is always a difficult problem due to environmental inference. Here, segmentation phase is highly concentrated by performing an initial spatial segmentation to achieve a minimal number of segmented regions while preserving the homogeneity criteria of each region. This paper aims in evaluating similarities based segmentation methods to compose the partition of objects in Infra-Red images. The output is a set of non-overlapping homogenous regions that compose the pixels of the image. These extracted regions are used as the initial data structure in feature extraction process. Experimental results conclude that h-maxima transformation provides better results for landmine detection by taking the advantage of the threshold. The relative performance of different conventional methods and proposed method are evaluated and compared using the Global Consistency Error and Structural Content. It proves that h-maxima gives significant results that definitely facilitate the landmine classification system more effectively.

Image segmentation for binocular stereoscopic dual-energy x-ray imaging

To successfully segment a transmission x-ray image the inherent blurring or structural unsharpness present in such an image must be taken into account. Initial work with an established wavelet-based segmentation algorithm resulted in an oversensitive performance with regard to the number of segmented regions detected. To improve on this performance, two segmentation algorithms are developed and tested. The approaches utilize a wavelet transform together with region merging and, alternatively, multilevel thresholding with region merging. These techniques outperform the ordinary wavelet method. Manually segmented images representing ground truth are used for a comparative evaluation of the segmentation techniques. Our work is part of an ongoing collaborative research program with the U.K. Home Office to analyze information extracted from dual-energy x-ray images for aviation security screening applications.

Utilization of the recursive shortest spanning tree algorithm for video-object segmentation by 2-D affine motion modeling

A novel video-object segmentation algorithm is proposed, which takes the previously estimated 2-D dense motion vector field as input and uses the generalized recursive shortest spanning tree method to approximate each component of the motion vector field as a piecewise planar function. The algorithm is successful in capturing 3-D planar objects in the scene correctly, with acceptable accuracy at the boundaries. The proposed algorithm is fast and requires no initial guess about the segmentation mask. Moreover, it is a hierarchical scheme which gives finest to coarsest segmentation results. The only external parameter needed by the algorithm is the number of segmented regions that essentially control the level at which the coarseness the algorithm would stop. The proposed algorithm improves the "analysis model" developed in the European COST211 framework.