Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation

COVID-19 causes an ongoing worldwide pandemic situation. The non-discovery of specialized drugs and/or any other kind of medicines makes the situation worse. Early diagnosis of this disease will be certainly helpful to start the treatment early and also to bring down the dire spread of this highly infectious virus. This article describes the proposed novel unsupervised segmentation method to segment the radiological image samples of the chest area that are accumulated from the COVID-19 infected patients. The proposed approach is helpful for physicians, medical technologists, and other related experts in the quick and early diagnosis of COVID-19 infection. The proposed approach will be the SUFEMO (SUperpixel based Fuzzy Electromagnetism-like Optimization). This approach is developed depending on some well-known theories like the Electromagnetism-like optimization algorithm, the type-2 fuzzy logic, and the superpixels. The proposed approach brings down the processing burden that is required to deal with a considerably large amount of spatial information by assimilating the notion of the superpixel. In this work, the EMO approach is modified by utilizing the type 2 fuzzy framework. The EMO approach updates the cluster centers without using the cluster center updation equation. This approach is independent of the choice of the initial cluster centers. To decrease the related computational overhead of handling a lot of spatial data, a novel superpixel-based approach is proposed in which the noise-sensitiveness of the watershed-based superpixel formation approach is dealt with by computing the nearby minima from the gradient image. Also, to take advantage of the superpixels, the fuzzy objective function is modified. The proposed approach was evaluated using both qualitatively and quantitatively using 310 chest CT scan images that are gathered from various sources. Four standard cluster validity indices are taken into consideration to quantify the results. It is observed that the proposed approach gives better performance compared to some of the state-of-the-art approaches in terms of both qualitative and quantitative outcomes. On average, the proposed approach attains Davies-Bouldin index value of 1.812008792, Xie-Beni index value of 1.683281, Dunn index value 2.588595748, and β index value 3.142069236 for 5 clusters. Apart from this, the proposed approach is also found to be superior with regard to the rate of convergence. Rigorous experiments prove the effectiveness of the proposed approach and establish the real-life applicability of the proposed method for the initial filtering of the COVID-19 patients.

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

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 c-means 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.

A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach

Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment. The proposed approach uses a novel superpixel computation method which is helpful to effectively represent the pixel intensity information which is beneficial for the optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm optimization approach. So, a twofold contribution can be observed in this work which is helpful to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical impact of this work. Both qualitative and quantitative experimental results show the effectiveness of the proposed approach and also establish it as an effective computer-aided tool to fight against the COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β index are used to quantify the segmented results and it is observed that the proposed approach not only performs well but also outperforms some of the standard approaches. On average, the proposed approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and 9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a contribution to the community.

SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation

The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.

A morphology-based radiological image segmentation approach for efficient screening of COVID-19

Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely. The optimal size of the structuring element is selected using the Edge Content-based contrast matrix approach. After performing the opening by using the morphological reconstruction operation, further noise is eliminated using the closing-based morphological reconstruction operation. The original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose. It is preferable to manually determine the range of the infected region. If a region is greater than the upper bound then that region will be considered as an exceptional region and processed separately. Three standard metrics MSE, PSNR, and SSIM are used to quantify the outcomes. Both quantitative and qualitative comparisons prove the efficiency and real-life adaptability of this approach. The proposed approach is evaluated with the help of 400 different images and on average, the proposed approach achieves MSE 307.1888625, PSNR 23.7246505, and SSIM 0.831718459. Moreover, the comparative study shows that the proposed approach outperforms some of the standard methods and obtained results are encouraging to support the battle against the COVID-19.