Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds

Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle

Muscle regeneration process tracking and analysis aim to monitor the injured muscle tissue section over time and analyze the muscle healing procedure. In this procedure, as one of the most diverse cell types observed, white blood cells (WBCs) exhibit dynamic cellular response and undergo multiple protein expression changes. The characteristics, amount, location, and distribution compose the action of cells which may change over time. Their actions and relationships over the whole healing procedure can be analyzed by processing the microscopic images taken at different time points after injury. The previous studies of muscle regeneration usually employ manual approach or basic intensity process to detect and count WBCs. In comparison, computer vision method is more promising in accuracy, processing speed, and labor cost. Besides, it can extract features like cell/cluster size and eccentricity fast and accurately. In this thesis, we propose an automated quantifying and analysis framework to analyze the WBC in light microscope images of uninjured and injured skeletal muscles. The proposed framework features a hybrid image segmentation method combining the Localized Iterative Otsu’s threshold method assisted by neural networks classifiers and muscle edge detection. In specific, both neural network and convoluted neural network based classifiers are studied and compared. Via this framework, the CD68-positive WBC and 7/4-positive WBC quantification and density distribution results are analyzed for demonstrating the effectiveness of the proposed method

Algoritmos baseados em inteligência de enxames aplicados à multilimiarização de imagens

Orientador: Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 20/08/2018Inclui referências: p.117-122Área de concentração: Sistemas EletrônicosResumo: O processamento de imagens é uma área que cresce à medida que as tecnologias de geração e armazenamento de informações digitais evoluem. Uma das etapas iniciais do processamento de imagem é a segmentação, onde a multilimiarização é uma das técnicas de segmentação mais simples. Um focorelevante de pesquisa nesta área é o projeto de abordagens visando a separação de diferentes objetos na imagem em grupos, por meio de limiares, para facilitar assim a interpretação da informação contida na imagem. Uma imagem perde informação, ou entropia, quando é limiarizada. A equação de limiarização multiníveis de Kapur calcula, a partir dos limiares escolhidos, qual a quantidade de informação que uma imagem apresentará após a limiarização. Assim, pela maximização da equação de multimiliarização de Kapur, é possível determinar os limiares que retornam uma imagem com valor maior de entropia. Quanto maior a quantidade de limiares, maior a dificuldade para encontrar a melhor solução, devido ao aumento significativo da quantidade de possíveis soluções. O objetivo desta dissertação é de apresentar um estudo comparativodecinco algoritmos de otimização (meta-heurísticas de otimização)da inteligência de enxame, incluindo Otimização por Enxame de Partículas (PSO), Otimização por Enxame de Partículas Darwiniano (DPSO), Otimização por Enxame de Partículas Darwiniano de Ordem Fracionária (FO-DPSO), Otimizador baseado no comportamento dos Lobos-cinza (GWO) e Otimizador inspirado no comportamento da Formiga-leão (ALO), de forma a avaliarqual deles obtém a melhor solução e convergência em termos da função objetivo relacionada a entropia da imagem. Uma contribuição desta dissertação é a aplicação de diferentes meta-heurísticas de otimização ao problema de multilimiarização de imagens, assim como o estudo do impacto das suas variáveis de controle (hiperparâmetros) para o problema em questão.Nesta dissertação são apresentados resultados paraquatro imagens diferentes, sendo duas imagens registradas por satélite (Rio Hunza e Yellowstone) e outras duas imagens teste (benchmark) obtidas do Centro de Engenharia Elétrica e Ciência da Computação do MIT (Massachussetts Institute of Technology). Os resultados são comparados considerando a média e o desvio padrão da entropia de cada imagem resultante. Com base nos resultados obtidos conclui-se que o algoritmo mais indicado para o problema de multilimiarização de imagens dos avaliados é o GWO, pelo seu desempenho superior em relação aos outros algoritmos e pelas entropias das imagens resultantes serem satisfatórias. Palavras-chave: Segmentação de imagens. Multilimiarização. Inteligência de enxames. Otimização por enxame de partículas. Otimizador dos lobos-cinza. Otimizador formiga-leão.Abstract: Image processing is a field that grows as digital information storage and generation technologies evolution. One of the initial stages of image processing is segmentation procedure, where the multi level thresholding is one of the simplest segmentation approaches. A relevant research objective in this field is the design of approaches aimed at separating different objects in the image into groups, through thresholds, to facilitate the interpretation of the information contained in the image. An image loses information, or entropy, when it is thresholded. The Kapur multilevel thresholding equation calculates, from the chosen thresholds, how much information an image will present after the thresholding. Thus, by the maximization of the Kapur multilevel limiarization equation, it is possible to determine the thresholds that return an image with a larger value of entropy. The higher the amount of thresholds, the greater the difficulty in finding the best solution, due to the significant increase in the quantity of possible solutions. The objective of this dissertation is to present a comparative study between fiveoptimization metaheuristics of the swarm intelligence field, including Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO), Grey Wolf Optimizer (GWO) and the Ant lion behavioral optimizer (ALO), in order to identify which one gets the best solution and convergence in terms of the objective function and the entropy of the image. A contribution of this dissertation is the application of different optimization metaheuristics to the problem of multilimizing of images, as well as the study of the impact of its control variables (hyperparameters) on the problem in question. Experiments are conducted with four images, two images being recorded by satellite (Hunza River and Yellowstone) and two other test(benchmark) images obtained from MIT's (Massachussetts Institute of Technology) Electrical Engineering and Computer Science Center. The results are compared considering the mean and standard deviation values of each resulting image entropy.Based on the results obtained it is concluded that the most suitable algorithm for the problem of multilevel thresholding of images is the GWO, for its superior performance in relation to the other tested algorithms and satisfactory entropies of the resulting images. Key-words: Image segmentation. Multilevel thresholding. Kapur's entropy. Swarm intelligence. Particle swarm optimization. Grey wolf optimizer. Ant lion optimizer

Automatic Construction of Immobilisation Masks for use in Radiotherapy Treatment of Head-and-Neck Cancer

Current clinical practice for immobilisation for patients undergoing brain or head and neck radiotherapy is normally achieved using Perspex or thermoplastic shells that are moulded to patient anatomy during a visit to the mould room. The shells are “made to measure” and the methods currently employed to make them require patients to visit the mould room. The mould room visit can be depressing and some patients find this process particularly unpleasant. In some cases, as treatment progresses, the tumour may shrink and therefore there may be a need for a further mould room visits. With modern manufacturing and rapid prototyping comes the possibility of determining the shape of the shells from the CT-scan of the patient directly, alleviating the need for making physical moulds from the patients’ head. However, extracting such a surface model remains a challenge and is the focus of this thesis. The aim of the work in this thesis is to develop an automatic pipeline capable of creating physical models of immobilisation shells directly from CT scans. The work includes an investigation of a number of image segmentation techniques to segment the skin/air interface from CT images. To enable the developed pipeline to be quantitatively evaluated we compared the 3D model generated from the CT data to ground truth obtained by 3D laser scans of masks produced by the mould room in the frame of a clinical trial. This involved automatically removing image artefacts due to fixations from CT imagery, automatic alignment (registration) between two meshes, measuring the degree of similarity between two 3D volumes, and automatic approach to evaluate the accuracy of segmentation. This thesis has raised and addressed many challenges within this pipeline. We have examined and evaluated each stage of the pipeline separately. The outcomes of the pipeline as a whole are currently being evaluated by a clinical trial (IRAS ID:209119, REC Ref.:16/YH/0485). Early results from the trial indicate that the approach is viable

Object Tracking in Video Images based on Image Segmentation and Pattern Matching

The moving object tracking in video pictures [1] has attracted a great deal of interest in computer vision. For object recognition, navigation systems and surveillance systems [10], object tracking is an indispensable first-step. We propose a novel algorithm for object tracking in video pictures, based on image segmentation and pattern matching [1]. With the image segmentation, we can detect all objects in images no matter whether they are moving or not. Using image segmentation results of successive frames, we exploit pattern matching in a simple feature space for tracking of the objects. Consequently, the proposed algorithm can be applied to multiple moving and still objects even in the case of a moving camera. We describe the algorithm in detail and perform simulation experiments on object tracking which verify the tracking algorithm‘s efficiency. VLSI implementation of the proposed algorithm is possible. The conventional approach to object tracking is based on the difference between the current image and the background image. However, algorithms based on the difference image cannot simultaneously detect still objects. Furthermore, they cannot be applied to the case of a moving camera. Algorithms including the camera motion information have been proposed previously, but, they still contain problems in separating the information from the background. The proposed algorithm, consisting of four stages i.e. image segmentation, feature extraction as well as object tracking and motion vector determination [12]. Here Image Segmentation is done in 3 ways and the efficiency of the tracking is compared in these three ways, the segmentation techniques used are ―Fuzzy C means clustering using Particle Swarm Optimization [5],[6],[17]”, ”Otsu’s global thresholding [16]”, ”Histogram based thresholding by manual threshold selection”, after image segmentation the features of each object are taken and Pattern Matching [10],[11],[20] algorithm is run on consecutive frames of video sequence, so that the pattern of extracted features is matched in the next frame , the motion of the object from reference frame to present frame is calculated in both X and Y directions, the mask is moved in the image accordingly, hence the moving object in the video sequences will be tracked

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision