Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

Real-Time Digital Image Stabilization Based on Contour

A new digital image stabilization method is proposed for real-time application based on image contour. The image intensities are projected to several gray levels by thresholding before extracting contour points. Matching position could be found using these contour points. All pixels are still used for refined matching near the matching position. This algorithm is more robust against changes in illumination and noise affection. The adaptive global motion judgement can remove the affection of intruding object. All those are realized on normally available PC.

Automatic Generation of Water Masks from RapidEye Images

Water is a very important natural resource and it supports all life forms on earth. It is used by humans in various ways including drinking, agriculture and for scientific research. The aim of this research was to develop a routine to automatically extract water masks from RapidEye images, which could be used for further investigation such as water quality monitoring and change detection. A Python-based algorithm was therefore developed for this particular purpose. The developed routine combines three spectral indices namely Simple Ratios (SRs), Normalized Green Index (NGI) and Normalized Difference Water Index (NDWI). The two SRs are calculated between the NIR and green band, and between the NIR and red band. The NGI is calculated by rationing the green band to the sum of all bands in each image. The NDWI is calculated by differencing the green to the NIR and dividing by the sum of the green and NIR bands. The routine generates five intermediate water masks, which are spatially intersected to create a single intermediate water mask. In order to remove very small waterbodies and any remaining gaps in the intermediate water mask, morphological opening and closing were performed to generate the final water mask. This proposed algorithm was used to extract water masks from some RapidEye images. It yielded an Overall Accuracy of 95% and a mean Kappa Statistic of 0.889 using the confusion matrix approach.

像增强器视场缺陷检测方法研究

在数字式像增强器性能综合检测系统的基础上,研究了像增强器视场缺陷的检测方法。通过数字视频摄像机采集缺陷图像,利用数字图像处理方法确定缺陷性质,缺陷处理算法基于图像灰度特征而不是边缘特征提取。该方法适用于对检测对象有具体量化和定位要求,而不是简单的只有数量和形状特征要求的场合。实验表明,该检测方法能够较准确地检测像增强器的缺陷。

New image processing algorithm for terminal guidance of multiple kinetic-energy impactors for disrupting hazardous asteroids

This paper describes the preliminary study results of developing a hypervelocity terminal intercept guidance system of a multiple kinetic-energy impactor vehicle(MKIV).The proposed MKIV system is intended to fragment or pulverize an asteroid of smaller than approximately 150 m in diameter that is detected with a mission lead time of shorter than 10 years,without using nuclear explosive devices.This paper focuses on the development of a new image processing algorithm based on Otsu’s method for the coordinated terminal intercept guidance and control of multiple kinetic-energy impactors employing visual and/or infrared sensors.A scaled polyhedron shape model of asteroid(216)Kleopatra is used as a fictional target asteroid.GPU-based simulation results demonstrate the feasibility of impacting a small irregular-shaped asteroid by using the proposed new image processing algorithm and a classical pulsed TPN(true proportional navigation)terminal guidance law.