Fusion-based contextually selected 3D Otsu thresholding for image segmentation

Abstract

Image segmentation is a method of subdividing an image into numerous meaningful regions or objects, which shows the image more informative for further analysis. Thresholding based methods are extensively used for image segmentation due to its easy implementation and low computational cost. However, histogram-based thresholding techniques are unable to deliberate three-dimensional contextual information of the image for optimum thresholds. In this paper, energy-curve is coupled with 3D Otsu function. Furthermore, in order to increase the quality of the processed image, a simple and effectual approach is proposed by using the concept of fusion, grounded on local contrast. The presentation of 3D Otsu algorithm is described to be poor when dealt with between-class variances over the aid of 3D histogram. To alleviate this limitation, the perception of the energy curve has been used to derive pixel intensity values and spatial information. Energy curve can help to recover the excellence of the thresholded image as it computes not only the value of the pixel but also its vicinity. The proposed energy based 3D Otsu with fusion (3D-Otsu Energy Fusion) method uses exhaustive search process to determine the optimal threshold values. The proposed technique produces better-processed results as compared to rest methods.