The coronavirus disease 2019 (COVID-19) pandemic rapidly spread globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is a positive-sense single-stranded RNA virus with a reported fatality rate ranging from 1% to 7%, and people with immune-compromised conditions, children, and older adults are particularly vulnerable. Respiratory failure and cytokine storm-induced multiple organ failure are the major causes of death. This article highlights the innate and adaptive immune mechanisms of host cells activated in response to SARS-CoV-2 infection and possible therapeutic approaches against COVID-19. Some potential drugs proven to be effective for other viral diseases are under clinical trials now for use against COVID-19. Examples include inhibitors of RNA-dependent RNA polymerase (remdesivir, favipiravir, ribavirin), viral protein synthesis (ivermectin, lopinavir/ritonavir), and fusion of the viral membrane with host cells (chloroquine, hydroxychloroquine, nitazoxanide, and umifenovir). This article also presents the intellectual groundwork for the ongoing development of vaccines in preclinical and clinical trials, explaining potential candidates (live attenuated-whole virus vaccines, inactivated vaccines, subunit vaccines, DNA-based vaccines, protein-based vaccines, nanoparticle-based vaccines, virus-like particles and mRNA-based vaccines). Designing and developing an effective vaccine (both prophylactic and therapeutic) would be a long-term solution and the most effective way to eliminate the COVID-19 pandemic.

The watershed segmentation methods are essential methods, to be considered for quick results in image handling and analysis. However, the main problem arises in produced image because it causes excess segmentation and noise. This research is conducted to improve this presented algorithm based on the mathematical morphology and filters to minimize flaws mentioned in that paper. Objective of this research is to find the gaps in the existing literary works. In most cases, themarker based segmentation is best because it marks the part of segment. The working of this proposed algorithm is checked by optimization of the part that is still an area of research.

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