Morphological Segmentation with Modified Otsu’s Method and Multi-Scale Morphological Processing for Shape Recognition

Motological segmentation is essential in the domains of medical imaging, object recognition, and remote sensing. Classical approaches, such as Otsu's thresholding, fail miserably in the presence of noise, light, and complex backgrounds. These concerns are addressed by creating Modified Otsu's Method (MOM). Slow and precise segmentation, adaptive thresholding, and morphological processes are two new areas that MOM aims to tackle. The proposed MOM makes both local and global adjustments taking into account the image and text in order to mitigate the impact of variations in light intensity and noise level. Accurate segmentation and less noise artefacts are achieved by the use of dilation and erosion. On datasets including medical, nature, and remote sensing images, the suggested MOM algorithm achieves better results than previous top-tier segmentation approaches as well as Otsu's approach to generic thresholding based on experimental quantities. This study investigates the optimal execution time of the method for image processing in real time in terms of computing cost. Automated safety devices and medical imaging analysis are two examples of applications where MOM stands out for its exceptional performance in picture segmentation. Therefore, in order to make MOM more versatile and effective in a wide range of imaging contexts, future studies will use machine learning methods.