Hybrid leader corona virus herd optimizer with multilevel thresholding techniques for foreground and background image segmentation

In computer vision and image processing, an important role is given to image segmentation. As it can accurately extract and identify specific regions within an image. One of the key challenges that remain in this is achieving precise recognition and extraction of segmented regions. This can be solved by multilevel optimization techniques and this offers an effective solution. Recently, multilevel thresholding has emerged as an important technique for image segmentation as the separation of image pixels into various classes by the selection of optimal threshold values. However, when the number of thresholds increases, the computational complexity of multilevel thresholding also increases. To address this issue, various optimization algorithms are employed by the researchers. Thus, the Hybrid optimization techniques are integrated to enhance the overall efficacy of image segmentation processes. Thus, this research devised a Multithreshold-Hybrid Leader Coronavirus Herd Optimizer+kernel based Bayesian fuzzy clustering (Multithreshold-HLCHO+kernel-BFC) for the segmentation of foreground and background images. Here, the noise from the input image is removed using the Non-Local Means (NLM) filter and also the region of interest (ROI) extraction is done. The foreground and background image segmentation is done using the multilevel thresholding techniques, wherein threshold values are optimally generated utilizing HLCHO with multiobjectives like Renyi, Masi and Tsallic. Moreover, the integration of Hybrid Leader Based Optimization (HLBO) with Coronavirus Herd Immunity Optimizer (CHIO) forms the HLCHO. Also, the kernel-based BFC is employed for the segmentation of background and foreground images. Finally, the segmented output is achieved by fusing these two outputs using a fusion process. Additionally, Multithreshold-HLCHO+kernel-BFC acquired a maximum value of 0.891 for dice coefficient, 37.174 dB for PSNR, 0.918 for uniformity measure, and 0.297 for Mean Squared error (MSE).