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This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 \u2212 L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.<\/jats:p>","DOI":"10.3390\/e23111429","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"1429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuncong","family":"Feng","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China"},{"name":"Artificial Intelligence Research Institute, Changchun University of Technology, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanru","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"},{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6039-9285","authenticated-orcid":false,"given":"Guishen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China"},{"name":"Artificial Intelligence Research Institute, Changchun University of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101876","DOI":"10.1016\/j.media.2020.101876","article-title":"Pairwise learning for medical image segmentation","volume":"67","author":"Wang","year":"2020","journal-title":"Med. 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