Determination of image bimodality thresholds for different intensity distributions

摘要

Between-class variance has been first proposed as a criterion function to determine an optimal threshold to segment images into nearly homogenous regions. This discriminant function is also widely used as a first step in iterative image segmentation methods such as Markov random field based methods to speed up the convergence. The between-class variance algorithm always computes an optimal threshold regardless of its validity. In this study, we established the threshold values (for bimodality) of the normalized (by total variance) between-class variance function for different distributions. The theoretical values of the bimodality thresholds for uniform and normal distributions are derived. The threshold values in the case of uniform, normal and poisson distributions were estimated through an image simulation approach. Experiments on simulated bimodal images showed that the threshold value for bimodality is dependent on the underlying noise distribution. The efficacy of the new threshold values was demonstrated on computer-simulated images as well as on actual images.