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On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.<\/jats:p>","DOI":"10.1515\/mathm-2019-0002","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T17:06:02Z","timestamp":1573146362000},"page":"29-44","source":"Crossref","is-referenced-by-count":1,"title":["Prior-based Hierarchical Segmentation Highlighting Structures of Interest"],"prefix":"10.1515","volume":"3","author":[{"given":"Amin","family":"Fehri","sequence":"first","affiliation":[{"name":"Center of Mathematical Morphology , Mines ParisTech, PSL Research University Paris"}]},{"given":"Santiago","family":"Velasco-Forero","sequence":"additional","affiliation":[{"name":"Center of Mathematical Morphology, Mines ParisTech , PSL Research University Paris"}]},{"given":"Fernand","family":"Meyer","sequence":"additional","affiliation":[{"name":"Center of Mathematical Morphology, Mines ParisTech , PSL Research University Paris"}]}],"member":"374","published-online":{"date-parts":[[2019,10,30]]},"reference":[{"key":"2022042707592435519_j_mathm-2019-0002_ref_001_w2aab3b7b2b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. 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