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In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation. [ABSTRACT FROM AUTHOR]

For proper homoeopathic identification of the medical image, image fusion has been proposed as a mandatory solution to obtain high-spectral and high-spectral spatial data. This article presents a complete fusion system for several types of medical images according to their multi-resolution, multi-scale transforms and the Modified Central Force Optimization (MCFO) technique. Four main techniques have been proposed for this purpose; Optimized Discrete Wavelet and Dual-Tree based fusion techniques as a multi-resolution transform. Besides, the optimized Non-Sub-Sampled Contourlet and Non-Sub-Sampled Shearlet as multi-scale fusion techniques. The perfect matching between input images and minimum artifacts after image registration can be achieved through four stages in the proposed fusion algorithms. First, the input medical image is initially decomposed into their coefficients, and the MCFO method establishes the optimal gain parameter values of the resulted coefficients. Finally, the adaptive histogram equalization and the histogram matching are applied for higher clearness and better visualization of information details. The proposed algorithms are evaluated using various datasets for different medical and surveillance applications through some quality metrics. The Experimental test outcomes indicate that the proposed fusion algorithms achieve good performance with high image quality and appreciated estimation metrics principles. Moreover, it provides better image visualization and minimum processing time, which helps diagnose diseases. [ABSTRACT FROM AUTHOR]

Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non‐Sub‐Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High‐Resolution (HR) image from the Low‐Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors. [ABSTRACT FROM AUTHOR]

Nowadays, optimization has become a brand methodology for different applications. One of the most promising fields for application of optimization is the image processing field, especially image fusion. A new effective deterministic optimization technique is the modified central force optimization (MCFO) that overcomes the low convergence rate drawback of the central force optimization (CFO). In this paper, the MCFO is applied with standard image fusion methods as a novel brand to improve the fusion efficiency either qualitatively or quantitatively. Intensity-hue-saturation (IHS), high-pass filtering (HPF), and discrete wavelet transform (DWT) are powerful standard techniques for satellite image fusion that are implemented with MCFO optimization in this paper. They are performed on satellite panchromatic (PAN) and multispectral (MS) images. The target of using the MCFO is to reduce some spectral and spatial distortions that may occur without optimization. Different qualitative indices have been used to validate the proposed approach comprising optimization for satellite image fusion. [ABSTRACT FROM AUTHOR]

In the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization‐based contourlet image fusion approach in addition to a comparative study for the performance of both multi‐resolution and multi‐scale geometric effects on fusion quality. An optimized multi‐scale fusion technique based on the Non‐Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal‐to‐noise ratio, Qab/f, and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications. [ABSTRACT FROM AUTHOR]

Highlights • Related works. • Modified Central Force Optimization (MCFO). • The proposed optimized medical image fusion system. • Local contrast enhancement techniques. • Simulation results and comparisons. Abstract This paper presents an efficient medical image fusion system based on Dual-Tree Complex Wavelet Transform (DT-CWT) and the Modified Central Force Optimization (MCFO) technique. The first step in the proposed system is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The DT-CWT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. A comparative study between the traditional spatial and transform domain fusion techniques and the proposed optimized DT-CWT fusion system is presented. The proposed fusion system is subjectively and objectively tested and evaluated with different fusion quality metrics. Simulation results demonstrate that the proposed optimized DT-CWT medical image fusion system based on MCFO and histogram matching achieves a superior performance with better image quality, much more details. These characteristics help in a more accurate medical diagnosis. [ABSTRACT FROM AUTHOR]

The central complex, a set of neuropils in the center of the insect brain, plays a crucial role in spatial aspects of sensory integration and motor control. Stereotyped neurons interconnect these neuropils with one another and with accessory structures. We screened over 5,000 Drosophila melanogaster GAL4 lines for expression in two neuropils, the noduli (NO) of the central complex and the asymmetrical body (AB), and used multicolor stochastic labeling to analyze the morphology, polarity, and organization of individual cells in a subset of the GAL4 lines that showed expression in these neuropils. We identified nine NO and three AB cell types and describe them here. The morphology of the NO neurons suggests that they receive input primarily in the lateral accessory lobe and send output to each of the six paired noduli. We demonstrate that the AB is a bilateral structure which exhibits asymmetry in size between the left and right bodies. We show that the AB neurons directly connect the AB to the central complex and accessory neuropils, that they target both the left and right ABs, and that one cell type preferentially innervates the right AB. We propose that the AB be considered a central complex neuropil in Drosophila. Finally, we present highly restricted GAL4 lines for most identified protocerebral bridge, NO, and AB cell types. These lines, generated using the split‐GAL4 method, will facilitate anatomical studies, behavioral assays, and physiological experiments. The authors use the multicolor flip‐out strategy in Drosophila to analyze neurons in two neuropils: the noduli of the central complex and the asymmetrical body. A set of split‐GAL4 lines is provided for previously published protocerebral bridge cell types as well as cell types presented here. [ABSTRACT FROM AUTHOR]

Carrier aggregation (CA) is a promising bandwidth extension technique which in turn increases the data rate. A scalable and flexible Orthogonal frequency-division multiplexing (OFDM) can be considered as a key element in 5G wireless communications. The combination of CA and OFDM is a prominent factor in LTE-A system followed by strict synchronicity. So, carrier frequency offset (CFO) is an important parameter to be considered and corrected. In carrier-aggregated OFDM (CA-OFDM) systems, multiple carrier frequency offsets (MCFOs) are a non linear synchronization error that induces inter carrier interference (ICI) to the system. To solve this problem, our self proposed Diversity Enhanced Particle Filter (DE-PF) with a novel resampling algorithm is used in downlink CA-OFDM receiver structure with Hadamard sequence based prominent matching algorithm to match the estimated MCFO to its corresponding Component Carrier (CC). Simulations prove that with DE-PF algorithm and proposed Hadamard matching algorithm improves MCFO estimation accuracy to a greater extent than the existing linear and non linear algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT Insects exhibit an elaborate repertoire of behaviors in response to environmental stimuli. The central complex plays a key role in combining various modalities of sensory information with an insect's internal state and past experience to select appropriate responses. Progress has been made in understanding the broad spectrum of outputs from the central complex neuropils and circuits involved in numerous behaviors. Many resident neurons have also been identified. However, the specific roles of these intricate structures and the functional connections between them remain largely obscure. Significant gains rely on obtaining a comprehensive catalog of the neurons and associated GAL4 lines that arborize within these brain regions, and on mapping neuronal pathways connecting these structures. To this end, small populations of neurons in the Drosophila melanogaster central complex were stochastically labeled using the multicolor flip-out technique and a catalog was created of the neurons, their morphologies, trajectories, relative arrangements, and corresponding GAL4 lines. This report focuses on one structure of the central complex, the protocerebral bridge, and identifies just 17 morphologically distinct cell types that arborize in this structure. This work also provides new insights into the anatomical structure of the four components of the central complex and its accessory neuropils. Most strikingly, we found that the protocerebral bridge contains 18 glomeruli, not 16, as previously believed. Revised wiring diagrams that take into account this updated architectural design are presented. This updated map of the Drosophila central complex will facilitate a deeper behavioral and physiological dissection of this sophisticated set of structures. J. Comp. Neurol. 523:997-1037, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Infrared, visible, and medical image fusion systems are mandatory solutions for obtaining more spatial and more spectral information in a single image for efficient object detection and medical diagnosis applications. In this paper, an optimized fusion system for multi-modality images based on the Non-sub-Sampled Shearlet Transform (NSST) with Modified Central Force Optimization (MCFO), histogram matching, and local contrast enhancement is presented. The proposed multi-modality image fusion system consists of four stages. The first stage is the image registration and then histogram matching of one of the images to the other to allow the same dynamic range for both images to minimize the fusion artifacts. The NSST is used after that to decompose the images to be fused into their coefficients. The NSST provides a better sparse image representation of highly localized coefficients, anisotropic directionality, and reduced pseudo-Gibbs artifacts. After that, the MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion system is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation, edge intensity, entropy, Peak Signal-to-Noise Ratio (PSNR), and Qab/f. Real infrared, visible, and medical datasets of different modalities are used to test the proposed system. Experimental results demonstrate that the proposed system achieves a superior performance with higher image quality, higher evaluation metrics values, and much more details in images. These characteristics help for more accuracy in applications such as object detection and medical diagnosis. [ABSTRACT FROM AUTHOR]