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Biol."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a scale\u2010adaptive superpixel algorithm relaxing the size\u2010uniformity criterion for medical images, especially pathological images. A new path\u2010based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color\u2010riching areas (flat areas).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size\u2010uniformly superpixel algorithms while retaining more details of images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state\u2010of\u2010the\u2010art algorithms on both quantitative and quantitative comparisons.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-021-0275","type":"journal-article","created":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T05:36:20Z","timestamp":1631943380000},"page":"264-275","source":"Crossref","is-referenced-by-count":0,"title":["Scale\u2010adaptive superpixels for medical images"],"prefix":"10.1002","volume":"10","author":[{"given":"Limin","family":"Sun","sequence":"first","affiliation":[{"name":"School of Software Shandong University Jinan 250100 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Software Shandong University Jinan 250100 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software Shandong University Jinan 250100 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,9]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"Ren X. and Malik J. 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