A new multilevel histogram thresholding approach using variational mode decomposition

Image segmentation is a technique of subdividing an image into numerous sections that converts an image into more expressive form that is easier to analyze. Histogram is one of the most widely used techniques for segmenting a digital image due to its simplicity. However, this method often leads to unsatisfactory segmentation performance because of abnormalities on gray level histogram. In this paper, we propose a technique for segmenting a digital image through multilevel iterative variational mode decomposition (VMD) using Renyi entropy. The VMD is employed first in order to decompose the gray-level histogram into corresponding sub-modes for analysis and attributes extraction. Splitting gray level histogram into various modes results in removal the unfavorable effects. Then, Renyi entropy is applied in order to find best threshold value for image segmentation. The feature set has been formulated by applying non-linear Renyi entropy on each of the modes extracted using VMD. The proposed technique has been tested on standard images and the experimental outcomes indicate that it can produce judicious segmentation outcomes compared to other techniques.