An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms

Abstract

Multilevel thresholding has been long considered as one of the most popular techniques for image segmentation. Multilevel thresholding outputs a gray scale image in which more details from the original picture can be kept, while binary thresholding can only analyze the image in two colors, usually black and white. However, two major existing problems with the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take an exceptionally long computation time; and defining a proper number of thresholds or levels that will keep most of the relevant details from the original image is a difficult task. In this study a new evaluation function based on the Kullback-Leibler information distance, also known as relative entropy, is proposed. The property of this new function can help determine the number of thresholds automatically. To offset the expensive computational effort by traditional exhaustive search methods, this study establishes a procedure that combines the relative entropy and meta-heuristics. From the experiments performed in this study, the proposed procedure not only provides good segmentation results when compared with a well known technique such as Otsu's method, but also constitutes a very efficient approach.

Quantitative Measurement of CSF in Patients with Spontaneous Intracranial Hypotension

“…The threshold-based algorithm of relative entropy has been proposed for use in clinical applications to enable reproducible and facile image segmentation. [9][10][11][12] With the advantages of selecting an optimal threshold value with local neighborhood information, the method could extract hyperintense CSF voxels from other hypointense background tissue automatically and robustly in 3D-SPACE wholespine MRM. After processing, we calculated CSF volumes by identifying all CSF-specific voxels from the tip of the odontoid process to the end of the dural sac.…”

Application of the path-repairing technique and virus optimization algorithm for the dimensional synthesis of four-bar mechanisms

“…In 2016, Liang and Cuevas Juarez published a paper [ 20 ] in which they proposed a new metaheuristic, called the virus optimization algorithm (VOA), and investigated the results obtained in solving eight benchmark functions. The algorithm was based on the authors’ previous works [ 21 , 22 ]. Although the original work by Liang and Cuevas Juarez was published recently [ 20 ], the VOA has been successfully applied to solve several optimization problems.…”

Image Thresholding Segmentation on Quantum State Space

“…Although these problems have been traditionally solved by considering the expectation maximization (EM) algorithm [14] or gradient-based methods [15,16], the methods are time consuming. Nonparametric approaches find the thresholds that separate regions of an image in an optimal manner based on discriminating criteria such as the between-class variance [17], cluster distance [18], entropy [19][20][21][22], etc. Nonparametric methods have shown the advantage of dispensing with the modeling thresholding.…”