A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images. (16th March 2017)

• Record Type: Journal Article
• Authors: Liu, Siyan; Shen, Xuanjing; Feng, Yuncong; Chen, Haipeng
• Other Names: Wei Guowei Academic Editor.
• Is Part Of: International journal of biomedical imaging. Volume 2017(2017)
• Publication Date: 2017-03-16
• Subjects: Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Imagerie pour le diagnostic; Imagerie médicale; Diagnostic imaging; Imaging systems in medicine; Diagnostic Imaging -- Periodicals; Electronic journals; Periodicals; 616.0754
• DOI: 10.1155/2017/9759414
• Languages: English
• ISSNs: 1687-4188

Abstract

Abstract : Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable atO ( L ) . Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.