{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T16:23:46Z","timestamp":1775924626216,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs),\n                      <jats:italic>n<\/jats:italic>\n                      thresholds are generated and then produce\n                      <jats:italic>n<\/jats:italic>\n                      thresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image\u2019s histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-022-00801-w","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T08:02:56Z","timestamp":1650960176000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Threshold estimation based on local minima for nucleus and cytoplasm segmentation"],"prefix":"10.1186","volume":"22","author":[{"given":"Simeon","family":"Mayala","sequence":"first","affiliation":[]},{"given":"Jonas Bull","family":"Haugs\u00f8en","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"issue":"Suppl","key":"801_CR1","doi-asserted-by":"publisher","first-page":"S15","DOI":"10.4103\/2153-3539.109883","volume":"4","author":"A Mathur","year":"2013","unstructured":"Mathur A, Tripathi AS, Kuse M. Scalable system for classification of white blood cells from Leishman stained blood stain images. J Pathol Inform. 2013;4(Suppl):S15.","journal-title":"J Pathol Inform"},{"issue":"1","key":"801_CR2","first-page":"63","volume":"14","author":"J Prinyakupt","year":"2015","unstructured":"Prinyakupt J, Pluempitiwiriyawej C. Segmentation of white blood cells and comparison of cell morphology by linear and Na\u00efve Bayes classifiers. Biomed Eng. 2015;14(1):63.","journal-title":"Biomed Eng"},{"key":"801_CR3","doi-asserted-by":"crossref","unstructured":"Hemalatha R, Thamizhvani T, Dhivya AJA, Joseph JE, Babu B, Chandrasekaran R. Active contour based segmentation techniques for medical image analysis. Med Biol Image Anal. 2018;17.","DOI":"10.5772\/intechopen.74576"},{"key":"801_CR4","unstructured":"Gonzalez RC, Woods RE. Digital image processing, 4th edn. 330 Hudson Street, New York, NY 10013;2008."},{"key":"801_CR5","doi-asserted-by":"crossref","unstructured":"Ismail A, Marhaban M. A simple approach to determine the best threshold value for automatic image thresholding. In: 2009 IEEE international conference on signal and image processing applications, pp. 162\u20136 (2009). IEEE.","DOI":"10.1109\/ICSIPA.2009.5478623"},{"key":"801_CR6","unstructured":"Lazar M, Hladnik A. Implementation of global and local thresholding algorithms in image segmentation of coloured prints. In: 35th international research conference IARIGAI, vol. 35 (2008)."},{"key":"801_CR7","unstructured":"Singh TR, Roy S, Singh OI, Sinam T, Singh K, et al. A new local adaptive thresholding technique in binarization. arXiv preprint arXiv:1201.5227 (2012)."},{"issue":"03","key":"801_CR8","first-page":"54","volume":"3","author":"R Firdousi","year":"2014","unstructured":"Firdousi R, Parveen S. Local thresholding techniques in image binarization. Int J Eng Comput Sci. 2014;3(03):54.","journal-title":"Int J Eng Comput Sci"},{"key":"801_CR9","doi-asserted-by":"crossref","unstructured":"Li Y, Zhu R, Mi L, Cao Y, Yao D. Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Comput Math Methods Med. 2016;2016.","DOI":"10.1155\/2016\/9514707"},{"issue":"1","key":"801_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-11-558","volume":"11","author":"Y-K Chan","year":"2010","unstructured":"Chan Y-K, Tsai M-H, Huang D-C, Zheng Z-H, Hung K-D. Leukocyte nucleus segmentation and nucleus lobe counting. BMC Bioinform. 2010;11(1):1\u201318.","journal-title":"BMC Bioinform"},{"key":"801_CR11","doi-asserted-by":"crossref","unstructured":"Theera-Umpon N. White blood cell segmentation and classification in microscopic bone marrow images. In: International conference on fuzzy systems and knowledge discovery, pp. 787\u2013796 (2005). Springer.","DOI":"10.1007\/11540007_98"},{"issue":"2","key":"801_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb PF, Huttenlocher DP. Efficient graph-based image segmentation. Int J Comput Vis. 2004;59(2):167\u201381.","journal-title":"Int J Comput Vis"},{"key":"801_CR13","unstructured":"Shi J, Malik J. Normalized cuts and image segmentation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp. 731\u2013737 (1997). IEEE."},{"issue":"8","key":"801_CR14","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell. 2000;22(8):888\u2013905.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"801_CR15","doi-asserted-by":"crossref","unstructured":"Salem NM. Segmentation of white blood cells from microscopic images using k-means clustering. In: 2014 31st national radio science conference (NRSC), pp. 371\u2013376 (2014). IEEE.","DOI":"10.1109\/NRSC.2014.6835098"},{"key":"801_CR16","doi-asserted-by":"crossref","unstructured":"Miao H, Xiao C. Simultaneous segmentation of leukocyte and erythrocyte in microscopic images using a marker-controlled watershed algorithm. Comput Math Methods Med. 2018;2018.","DOI":"10.1155\/2018\/7235795"},{"key":"801_CR17","doi-asserted-by":"crossref","unstructured":"Al-Dulaimi K, Tomeo-Reyes I, Banks J, Chandran V. White blood cell nuclei segmentation using level set methods and geometric active contours. In: 2016 international conference on digital image computing: techniques and applications (DICTA), pp. 1\u20137 (2016). IEEE.","DOI":"10.1109\/DICTA.2016.7797097"},{"issue":"2","key":"801_CR18","doi-asserted-by":"publisher","first-page":"92","DOI":"10.4103\/2228-7477.205503","volume":"7","author":"N Ghane","year":"2017","unstructured":"Ghane N, Vard A, Talebi A, Nematollahy P. Segmentation of white blood cells from microscopic images using a novel combination of k-means clustering and modified watershed algorithm. J Med Signals Sens. 2017;7(2):92.","journal-title":"J Med Signals Sens"},{"key":"801_CR19","doi-asserted-by":"crossref","unstructured":"Kuse M, Sharma T, Gupta S. A classification scheme for lymphocyte segmentation in h&e stained histology images. In: International conference on pattern recognition, pp. 235\u2013243 (2010). Springer.","DOI":"10.1007\/978-3-642-17711-8_24"},{"issue":"1","key":"801_CR20","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/s12575-009-9011-2","volume":"11","author":"F Sadeghian","year":"2009","unstructured":"Sadeghian F, Seman Z, Ramli AR, Kahar BHA, Saripan M-I. A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced. 2009;11(1):196.","journal-title":"Biol Proced"},{"key":"801_CR21","doi-asserted-by":"publisher","first-page":"113211","DOI":"10.1016\/j.eswa.2020.113211","volume":"149","author":"PP Banik","year":"2020","unstructured":"Banik PP, Saha R, Kim K-D. An automatic nucleus segmentation and CNN model based classification method of white blood cell. Expert Syst Appl. 2020;149:113211.","journal-title":"Expert Syst Appl"},{"issue":"7","key":"801_CR22","doi-asserted-by":"publisher","first-page":"201800488","DOI":"10.1002\/jbio.201800488","volume":"12","author":"H Fan","year":"2019","unstructured":"Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y. Leukocytemask: an automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophotonics. 2019;12(7):201800488.","journal-title":"J Biophotonics"},{"key":"801_CR23","doi-asserted-by":"publisher","first-page":"107006","DOI":"10.1016\/j.asoc.2020.107006","volume":"101","author":"Y Lu","year":"2021","unstructured":"Lu Y, Qin X, Fan H, Lai T, Li Z. Wbc-net: a white blood cell segmentation network based on unet++ and resnet. Appl Soft Comput. 2021;101:107006.","journal-title":"Appl Soft Comput"},{"key":"801_CR24","doi-asserted-by":"crossref","unstructured":"Mittal A, Dhalla S, Gupta S, Gupta A. Automated analysis of blood smear images for leukemia detection: a comprehensive review. ACM Comput Surv (CSUR) (2022).","DOI":"10.1145\/3514495"},{"issue":"1","key":"801_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3332-1","volume":"21","author":"F Long","year":"2020","unstructured":"Long F. Microscopy cell nuclei segmentation with enhanced u-net. BMC Bioinform. 2020;21(1):1\u201312.","journal-title":"BMC Bioinform"},{"issue":"11","key":"801_CR26","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 2012;34(11):2274\u201382.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"801_CR27","unstructured":"Beucher S, et al. The watershed transformation applied to image segmentation. Scan Microsc Suppl. 1992;299."},{"key":"801_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-04919-8","volume-title":"B\u00e9zier and B-spline techniques","author":"H Prautzsch","year":"2002","unstructured":"Prautzsch H, Boehm W, Paluszny M. B\u00e9zier and B-spline techniques, vol. 6. Berlin: Springer; 2002."},{"key":"801_CR29","first-page":"3","volume-title":"Spline methods draft","author":"T Lyche","year":"2008","unstructured":"Lyche T, Morken K. Spline methods draft. Oslo: Department of Informatics, Center of Mathematics for Applications, University of Oslo; 2008. p. 3\u20138."},{"key":"801_CR30","doi-asserted-by":"crossref","unstructured":"Acevedo A, Merino A, Alf\u00e9rez S, Molina \u00c1, Bold\u00fa L, Rodellar J. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief. 2020;105474.","DOI":"10.1016\/j.dib.2020.105474"},{"key":"801_CR31","doi-asserted-by":"crossref","unstructured":"Zheng X, Wang Y, Wang G, Liu J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107.","DOI":"10.1016\/j.micron.2018.01.010"},{"key":"801_CR32","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"...Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, Fern\u00e1ndez del R\u00edo J, Wiebe M, Peterson P, G\u00e9rard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE. Array programming with NumPy. Nature. 2020;585:357\u201362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2.","journal-title":"Nature"},{"key":"801_CR33","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"...Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat I, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P. SciPy 1.0 contributors: SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods. 2020;17:261\u201372. https:\/\/doi.org\/10.1038\/s41592-019-0686-2.","journal-title":"Nat Methods"},{"issue":"5","key":"801_CR34","first-page":"713","volume":"17","author":"P-S Liao","year":"2001","unstructured":"Liao P-S, Chen T-S, Chung P-C, et al. A fast algorithm for multilevel thresholding. J Inf Sci Eng. 2001;17(5):713\u201327.","journal-title":"J Inf Sci Eng"},{"key":"801_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","volume":"166","author":"D Stutz","year":"2018","unstructured":"Stutz D, Hermans A, Leibe B. Superpixels: an evaluation of the state-of-the-art. Comput Vis Image Underst. 2018;166:1\u201327.","journal-title":"Comput Vis Image Underst"},{"key":"801_CR36","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 234\u2013241 (2015). Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"801_CR37","unstructured":"Chollet F, et al. Keras. https:\/\/github.com\/fchollet\/keras."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00801-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00801-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00801-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T09:42:05Z","timestamp":1675417325000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00801-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,26]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["801"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00801-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1017730\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,26]]},"assertion":[{"value":"25 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"77"}}