Binarization of medical images based on the recursive application of mean shift filtering: Another algorithm
Digital Signal Processing Group, Institute of Cybernetics, Mathematics and Physics (ICIMAF), La Habana, Cuba
Abstract: Binarization is often recognized to be one of the most important steps in most high-level image analysis systems, particularly for object recognition. Its precise functioning highly determines the performance of the entire system. According to many researchers, segmentation finishes when the observer’s goal is satisfied. Experience has shown that the most effective methods continue to be the iterative ones. However, a problem with these algorithms is the stopping criterion. In this work, entropy is used as the stopping criterion when segmenting an image by recursively applying mean shift filtering. Of this way, a new algorithm is introduced for the binarization of medical images, where the binarization is carried out after the segmented image was obtained. The good performance of the proposed method; that is, the good quality of the binarization, is illustrated with several experimental results. In this paper a comparison was carried out among the obtained results with this new algorithm with respect to another developed by the author and collaborators previously and also with Otsu’s method.
Keywords: image segmentation, mean shift, algorithm, entropy, Otsu’s method
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF]