Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy cmeans clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.

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“…5. Black hole optimization [55] Modified cuckoo search [57] Fruit fly optimization [56] FEMO [58] Our proposed approach Fig. 4.…”

Section: B Experimental Resultsmentioning

confidence: 99%

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images

Ding

Chakraborty

Mali

et al. 2022

IEEE Trans. Fuzzy Syst.

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“…Image segmentation is a necessary image processing task that applied to discriminate region of interests (ROIs) from the area of outsides. Also, image segmentation can extract critical features, including the shape of tissues, and texture 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

207

141

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Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

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“…Its main goal is to distinguish the region of interest (ROI) from the area of outside. Moreover, it also enables to extract important features, for example, texture, and shape of tissues [6][7][8]. Recent advances in the field of medical imaging show that medical images can be heavily used in many medical procedures.…”

Section: Introductionmentioning

confidence: 99%

Automatic clustering method to segment COVID-19 CT images

et al. 2021

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Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

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Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

Cited by 14 publications

References 35 publications

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Automatic clustering method to segment COVID-19 CT images

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