A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

• Autorzy: Anilkumar K.K. , Manoj V.J. , Sagi T.M.

Identyfikatory

DOI

10.1016/j.bbe.2020.08.010

• Języki publikacji: EN

Abstrakty

EN

Leukemia is an abnormal proliferation of leukocytes in the bone marrow and blood and it is usually diagnosed by the pathologists by observing the blood smear under a microscope. The count of various cells and their morphological features are used by the pathologists to identify and classify leukemia. An abnormal increase in the count of immature leukocytes along with a reduced count of other blood cells may be an indication of leukemia. The Pathologist may then recommend for bone marrow examination to confirm and identify the specific type of leukemia. These conventional methods are time consuming and may be affected by the skill and expertise of the medical professionals involved in the diagnostic procedures. Image processing based methods can be used to analyze the microscopic smear images to detect the incidence of leukemia automatically and quickly. Image segmentation is one of the very important tasks in processing and analyzing medical images. In the proposed paper an attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia. The available works in the related area are reviewed based on the segmentation method used. It is learnt that even though there are many studies for detection of acute leukemia only a very few studies are there for the detection of chronic leukemia. There are a few related review studies available in the literature but, none of the works classify the previous studies based on the segmentation method used.

Słowa kluczowe

EN

leukemia; image segmentation; blood smear; leukocytes; image processing

PL

białaczka; segmentacja obrazu; rozmaz krwi; leukocyty; przetwarzanie obrazu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 4
• Strony: 1406--1420
• Opis fizyczny: Bibliogr. 119 poz., rys., tab., wykr.

Twórcy

autor

Anilkumar K.K.

• Department of Electronics & Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Pulincunnu P.O., Alappuzha District, Kerala State 688504, India

autor

Manoj V.J.

• Department of Electronics & Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science And Technology, Pulincunnu P.O., Alappuzha District, Kerala State 688504, India

autor

Sagi T.M.

• Department of Medical Lab Technology, School of Medical Education, Center for Professional and Advanced Studies, Gandhinagar P.O., Kottayam District, Kerala State 686008, India

Bibliografia

• [1] The Leukemia & Lymphoma Society, New York. https://www.ils.org/facts-and-statistics [Accessed 16 November 2019].
• [2] Cancer Research UK. http://www.cancerresearchuk.org [Accessed 16 November 2019].
• [3] Henry JB. Clinical diagnosis and management by laboratory methods. 17th ed. Philadelphia: W.B. Saunders Company; 1989.
• [4] Gonzalez RC, Woods RE. Digital image processing. 2nd ed. Pearson Education Asia; 2002.
• [5] Jain AK. Fundamentals of digital image processing. 1st ed. Pearson Education; 2003.
• [6] Xian M, Yingtao, Cheng HD, FeiXu, Zhang B, Ding J. Automatic breast ultrasound image segmentation: a survey. Pattern Recogn 2018;79:340–55.
• [7] Garcia-Lamont F, Cervantes J, Lopez A, Rodrigues L. Segmentation of images by color features: a survey. Neurocomputing 2018;292:1–27.
• [8] Saraswath M, Arya KV. Automated microscopic image analysis for leukocytes identification: a survey. Micron 2014;65:20–33.
• [9] Zaitoun NM, Aqel MJ. Survey on image segmentation techniques. Proc International conference on Communication, Management and Information Technology (ICCMIT), Proceedia Computer Science, vol. 65. 2015. pp. 797–806.
• [10] Hedge RB, Prasad K, Hebbar H. Peripheral blood smear analysis using image processing approach for diagnostic purposes: a review. Biocybern Biomed Eng 2018;38:467–80.
• [11] Alsalem MA, Zaidan AA, Zaidan BB, hashim M, Madhloom HT, Azeez ND. A review of auotomatic detection and classification of acute leukemia: coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. Comput Methods Programs Biomed 2018;158:93–112.
• [12] Rodellar J, Alferez S, Acevedo A, Molina A, Merino A. Image processing and machine learning in the morphological analysis of blood cells. Int J Lab Hematol 2018;40:46–53.
• [13] Komura D, Ishikawa S. Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J 2018;16:34–42.
• [14] Adollah R, Mashore MY, Mohd Nasir NF, Rosline H, Mahsin H, Adilah H. Blood cell image segmentation: a review. Proc 4th Kuala Lumpur International Conference on Biomedical Engineering; 2008.
• [15] Bagasjvara RG, Candradewi I, Hartati S, Harjoko A. Automated detection and classification techniques of acute leukemia using image processing: a review. Proc 2nd International Conference on Science and Technology- Computer (ICST); 2016.
• [16] Devi SS, Kumar R, Laskar RH. Recent advances on erythrocyte image segmentation for biomedical applications. Proc Fourth International Conference on Soft Computing for Problem Solving; 2014.
• [17] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6.
• [18] Zack GW, Rogers WE, Latt SA. Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 1977;25(7):741–53.
• [19] Dhanachandra N, Manglem K, JinaChanu Y. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Proc Eleventh International Multi-Conference on Information Processing (IMCIP); 2015.
• [20] Jin X, Han J. K-medoids clustering. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning. Boston, MA: Springer; 2011.
• [21] Zadeh L. Fuzzy sets. Inform Control 1965;8:338–53.
• [22] Kirindis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 2010;19(5):1328–37.
• [23] Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J Cybern 1974;3(3):32–57.
• [24] Bezdek JC. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press; 1981.
• [25] Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern 2004;34 (4):1907–16.
• [26] Naz S, Majeed H, Irshad H. Image segmentation using fuzzy clustering: a survey. Proc 6th International Conference on Emerging Technologies (ICET); 2010.
• [27] Bharathi S, Venkatesan P. A survey on image segmentation by fuzzy c-means clustering techniques. Proc 2nd International Conference on Applied and Theoretical Computing and Communication Technology; 2016.
• [28] Graves D, Pedrycz W. Fuzzy C-means, Gustafson-Kessel FCM, and kernel-based FCM: a comparative study. Anal Des Intell Syst Soft Comput Techn 2007;41:140–9.
• [29] Pawlak Z. Rough sets. Int J Comput Inform Sci 2004;11:341– 56. 1907–1916.
• [30] Zhang Q, Xie Q, Wang G. A survey on rough set theory and its applications. CAAI Trans Intell Technol 2016;I:323–33.
• [31] Mitra S, Banka H, Pedryez W. Rough-fuzzy collaborative clustering. IEEE Trans Syst Man Cybern Part B Cybern 2006;36(4):795–805.
• [32] Peters G, Crespo F, Lingras P, Weber R. Soft-clustering- fuzzy and rough approaches and their extensions and derivatives. Int J Approx Reason 2013;54:307–22.
• [33] Lai JZC, Juan EYT, Lai FJC. Rough clustering using generalised fuzzy clustering algorithm. Pattern Recogn 2013;46:2538–47.
• [34] Kass M, Witkin A, Terzopoulos. Snakes: active contour models. Int J Comput Vision 1988;1(4):321–31.
• [35] Putzu L, Caocci G, Di Ruberto C. Leukocyte classification for leukemia detection using image processing techniques. Artif Intell Med 2014;62:179–91.
• [36] Rawat J, Sigh A, Bhadauria HS, Virmani J, Devgun JS. Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybern Biomed Eng 2017;37:637–54.
• [37] Rawat J, Sigh A, Bhadauria HS, Virmani J, Devgun JS. Classification of acute lymphoblastic leukemia using hybrid hierarchical classifiers. Multimedia Tools Appl 2017;76:19057–85.
• [38] Rawat J, Sigh A, Bhadauria HS, Virmani J. Computer aided diagnostic system for detection of leukemia using microscopic images. Proc 4th International Conference on Eco-friemdly and Communication systems, Proceedia Computer Science, vol. 70. 2015. pp. 748–56.
• [39] Patel N, Mishra A. Automated leukemia detection using microscopic images. Proc Second International Symposium on Computer Vision and the Internet (VisionNet'15). Procedia Computer Science, vol. 58. 2015. pp. 635–42.
• [40] Shankar V, Deshpande MM, Chaitra N, Aditi S. Automatic detection of acute lymphoblastic leukemia using image processing. Proc IEEE International Conference on Advances in Computer Applications; 2016.
• [41] Sahlol AT, Abdeldaim AM, Hassanien AE. Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Comput 2019;23 (15):6345–60.
• [42] Scotti F. Automatic morphological analysis for acute leukemia identification in peripheral blood microscopic images. Proc IEEE International Conference on Computational Intelligence for Measurement Systems and Applications; 2005.
• [43] Poon SSS, Ward RK, Palcic B. Automated image detection and segmentation in blood smears. Cytometry 1992;13:766–74.
• [44] Buavirat S, ChonawatSrisa-an.. Classification for acute lymphocytic leukemia using feature extraction and neural networks in white blood cell stained images. Proc 3rd International Symposium on Biomedical Engineering; 2008.
• [45] Skrobanski S, Pavlids S, Ismail W, Hassan R, Counsell S, swift S. Use of general purpose GPU programming to enhance the classification of leukemia blast cells in blood smear images. Proc IDA; 2012.
• [46] Arslan S, Ozyurek E, Gunduz-Demir C. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry Part A 2014;85A:480–90.
• [47] Jasmine Begum AR, Razak AT. Diagnosing leukemia from microscopic images using image analysis and processing techniques. Proc World Congress on Computing and Communication Technologies; 2017.
• [48] Fakhouri HN, Al-Sharaeh SH. A hybrid methodology for automation the diagnosis of leukemia based on quantitative and morphological feature analysis. Mod Appl Sci 2018;12(3):56–73.
• [49] Ananthi VP, Balasubramaniam P. A new thresholding technique based on fuzzy set as an application to leukocytes nucleus segmentation. Comput Methods Programs Biomed 2016;(134):165–77.
• [50] Safuan SNM, Tomari MRM, Wan Zakaria WN. White blood cell counting analysis in blood smear images using various color segmentation methods. Measurement 2018;116:543–55.
• [51] Cao H, Liu H, Song E. A novel algorithm for segmentation of leukocytes in peripheral blood. Biomed Signal Process Control 2018;45:10–21.
• [52] Nazlibilek S, Karacor D, Ercan T, Sazli MH, Ege OKY. Automatic segmentation, counting, size determination and classification of white blood cells. Measurement 2014;55:59–65.
• [53] Huang D-C, Hung K-D, Chan Y-K. A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J Syst Softw 2014;85:2104–18.
• [54] Sholeh FI. White blood cell segmentation for fresh blood smear images. Proc ICACSIS; 2013.
• [55] Jati A, Singh G, Mukherjee R, Ghosh M, Konar A, Chakraborty C, et al. Automatic nucleus segmentation by intuitionistic fuzzy divergence based thresholding. Micron 2014;58:55–65.
• [56] Wang Y, Cao Y. Quick leukocyte nucleus segmentation in leukocyte counting. Comput Math Methods Med 2019;2019. http://dx.doi.org/10.1155/2019/3072498. Article ID 3072498, 10 pages.
• [57] Ghosh M, das D, Chakraborty C, Ray AK. Automated leukocytes recognition using fuzzy divergence. Micron 2010;41:840–6.
• [58] Kuse M, Sharma T, Gupta S. A classification scheme for lymphocyte segmentation in H & E stained histology images. Proc International conference on Patttern Recognition; 2010.
• [59] HaswaniAbdHalim N, Mashor MY, Hassan R. Automatic blast counting for acute leuklemia based on blood samples. Int J Res Rev Comput Sci 2011;2(4):971–6.
• [60] Tomari R, Wan Zakaria WN, Abdul Jamil MM, Nor FM, Fuad NFN. Computer aided system for red blood cell classification in blood smear image. Proc International Conference on Robot PRIDE 2013-2014, Procededia Computer Science, vol. 42. 2014. pp. 206–13.
• [61] Agaian S, Madhukar M, Chronopoulos AT. Automated screening system for acute mylogenous leukemia detection in blood microscopic images. IEEE Syst J 2014;8 (3):995–1004.
• [62] Su J, Liu S, Song J. A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia. Comput Methods Programs Biomed 2017;152:115–23.
• [63] Laosai J, Chamnongthai K. Classification of acute leukemia using medical-knowledge-based morphology and CD marker. Biomed Signal Process Control 2018;44:127–37.
• [64] Mohapatra S, Patra D, Satpathy S. Unsupervised blood microscopic image segmentation and leukemia detection using color based clustering. Int J Comput Inform Syst Ind Manag Appl 2012;4:477–85.
• [65] Mohapatra S, Patra D. Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images. Proc International Conference on Systems in Medicine and Biology; 2010.
• [66] Negum AS, Hassam OA, Kandil AH. A decision support system for acute leukemia classification based on digital microscopic images. Alex Eng J 2018;57(4):2319–32.
• [67] Kumar P, Udwadia SN. Automatic detection of acute myeloid leukemia for microscopic blood smear image. Proc International Conference on Advanced Communications and Informatics; 2017.
• [68] Acharya V, Kumar P. Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Med Biol Eng Comput 2019;57:1783–811. http://dx.doi.org/10.1007/s11517-019-01984-1.
• [69] Moshavash Z, Danyali H, Helfroush MS. An automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. J Dig Imaging 2018;3(5):702–17.
• [70] Jothi G, Hannah Inbarani H, Azar AT, Devi KR. Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 2019;31 (9):5175–94.
• [71] MoradiAmin M, Memari A, Samadzadehaghdam N, Kermani S, Talebi A. Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis. Microsc Res Technol 2016;79:908–16.
• [72] Mohapatra S, Patra D, Satpathi S. Image analysis of blood microscopic images for acute leukemia detection. Proc IEEE International Conference on Industrial Electronics, Control and Robotics; 2010.
• [73] Khosrosereshki MA, Menhaj MB. A fuzzy based classifier for diagnosis of acute lymphoblastic leukemia using blood smear image processing. Proc 5th Iranian Joint Congress Fuzzy and Intelligent Sytems (CFIS); 2017.
• [74] Mohapatra S, Shekhar S, Patra D, Satpathy S. Fuzzy based blood image segmentation for automated leukemia detection. Proc IEEE International Conference on Devices and Communications (ICDeCom); 2011.
• [75] Mohapatra S, Patra D, Kumar K. Unsupervised leukocyte image segmentation using rough fuzzy clustering. ISRN Artif Intell 2012;1–12.
• [76] Ravikumar S, Shanmugam A. WBC image segmentation using modified fuzzy possibilistic c-means algorithm. J Theor Appl Inform Technol 2014;63(2):315–23.
• [77] Rejintal A, Aswini N. Image processing based leukemia cancer cell detection. Proc. IEEE International Conference on Recent Trends in Electronics Information Communication Technology; 2016.
• [78] Anilkumar KK, Manoj VJ, Sagi TM. Colour based image segmentation for automated detection of leukaemia: a comparison between CIELAB and CMYK colour spaces. Proc IEEE International Conference on Circuits and Systems in Digital Enterpprise Technology; 2018.
• [79] Jagadev P, Virani HG. Detection of leukemia and its types using image processing and machine learning. Proc International Conference on Trends in Electronics and Informatics; 2017.
• [80] Dhanachandra N, Manglem K, Chanu YJ. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Proc Comput Sci 2015;54:764–71.
• [81] Neoh SC, Srisukkham W, Zhang L, Todryk S, Greystoke B, Peng Lim C, et al. An intelligent decision support system for leukaemia diagnosis using microscopic blood images. Sci Rep 2015;5(14938).
• [82] Nee LH, Mashor MY, Hassan R. White blood cell segmentation for acute leukemia bone marrow images. Proc International Conference on Biomedical Enginnering; 2012.
• [83] Suryani E, Wiharto, Palgunadi S, Nurcahya Pradana TP. Classification of acute myelogenous leukemia (AML M2 and AML M3) using momentum back proppagation from watershed distance transform segmented images. J Phys: Conf Ser 2017;801:012044.
• [84] Alferez S, Merino A, Mujica LE, Ruiz M, Bigorra L, Rodellar J. Automatic classification of atypical lymphoid B cells using digital blood image processing. Int J Lab Hematol 2014;36:472–80.
• [85] Cabrera RJA, Legaspi CAP, Papa EJG, samonte RD, Acula DD. HeMatic: an automated leukemia detector with separation of overlapping blood cells through image processing and genetic algorithm. Proc IEEE International Conference on Applied System Innovation; 2017.
• [86] Maity M, Mungle T, Dhane D, Maiti AK, Chakraborty C. An ensemble rule learning approach for automated morphological classification of erythrocytes. J Med Syst 2017;41(56):1–14.
• [87] Biswas S, Ghoshal D. Blood cell detection using thresholding estimation based watershed transformation with sobel filter in frequency domain. Proc Comput Sci 2016;89:651–7.
• [88] Sharif JM, Miswan MF, Ngadi MA, Sah Hj Salam M, Bin Abdul Jamil MM. Red blood cell segmentation using masking and watershed algorithm: a preliminary study. Proc International Conference on Biomedical Engineering (ICoBE); 2012.
• [89] Gonzalez-Betancourt A, Rodriguez-Ribelta P, Meneses- Marcel A, et al. Automated marker identification using the random transform for watershed segmentation. IET Image Process 2017;11(3):183–9.
• [90] Kim K, Jeon J, Choi W, Kim P, Ho Y-S. Automatic cell classification in human's peripheral blood images based on morphological image processing. AI 2001: Advances in Artificial Intelligence. 2011. pp. 225–36.
• [91] Benomar ML, Chikh A, Descombes X, Benazzouz M. Multi features based approach for white blood cells segmentation and classification in peripheral blood and bone marrow images; 2019, hal-02279352.
• [92] Viswanathan P. Fuzzy C means detection of Leukemia based on Morphological Contour Segmentation. Proc Second International Symposium on Computer Vision and the Internet. Procedia Computer Science, vol. 58. 2015. pp. 84–90.
• [93] Escalante HJ, Montes-y-Gomez M, Gonzalez JA, Gomez-Gil P, Altamirano L, Carlos A Reys, et al. Acute leukemia classification by ensemble particle swarm model selection. Artif Intell Med 2012;55:163–75.
• [94] Reta C, Altamirano L, Gonzalez JA, DiazHernandez R, Peregrina H, Olmos I, et al. Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias. PLoS ONE 2015;10(6):e0130805. http://dx.doi.org/10.1371/journal.pone.0130805.
• [95] Mohapatra S, Patra D, Kumar S, Satpathy S. Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection. Biomed Eng Lett 2012;2:100–10.
• [96] Jha KK, Dutta HS. Mutual information based hybrid model and deepp learning for acute lymphoblastic leukemia detection in single cell blood smear images. Comput Methods Programs Biomed 2019;179:1–12.
• [97] Madhloom HT, Kareem SA, Ariffin H. A robust feature extraction and selection method for the recognition of lyphocytes versus acute lymphoblastic leukemia. Proc International Conference on Advanced Computer Science Applications and Technologies; 2012.
• [98] Vyavahare AJ, Thool RC. Automatic detection of acute lymphoblastic leukemia based on HSI colour space. Proc 4th International Conference on Advances in Recent Technologies in Communication and Computing; 2012.
• [99] Elsalamony HA. Healthy and unhealthy red blood cell detection in human blood smears using neural networks. Micron 2016;83:32–41.
• [100] Abdulhay E, Mohammed MA, Ibrahim DA, Arun Kumar N, Venkatraman V. Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscopic images. J Med Syst 2018;42(58).
• [101] Ko BC, Gim J-W, Nam J-Y. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 2011;42:695–705.
• [102] Li Q, Wang Y, Liu H, Jianbiao, Guo F. A combined spatial- spectral method for automated white blood cell segmentation. Opt Laser Technol 2013;54:225–31.
• [103] Liu Y, Cao F, Zhao J, Chu J. Segmentation of white blood cells image using adaptive location and iteration. IEEE J Biomed Health Inform 2017;21:1644–55.
• [104] Zhen X, Wang Y, Wang G, Liu J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 2018;1017:55–71.
• [105] Pan C, Park DS, Yoon S, Yang JC. Leukocyte image segmentation using simulated visual attention. Expert Syst Appl 2012;39:7479–94.
• [106] Ko BC, Gim JW, Nam JY. Cell image classification based on ensemble features and random forest. Electron Lett 2011;47(11.).
• [107] Shahin AI, Guo Y, Amin KM, Sharawi AA. White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Programs Biomed 2019;168:69–80.
• [108] Sabino DMU, da Fontoura Costa L, Rizzatti EG, Zago MA. A texture approach to leukocyte recognition. Real-Time Imaging 2004;10:205–16.
• [109] Rawat J, Sigh A, Bhadauria HS, Virmani J, Devgun JS. Leukocyte classification using adaptive neuro-fuzzy inference system in microscopic blood images. Arab J Sci Eng 2018;43(12):7041–58.
• [110] Piuri V, Scotti F. Morphological classification of blood leucocytes by microscope images. Proc IEEE International Conference on Computational Intelligence for Measurement Systems and Applications; 2004.
• [111] Theerapattanakul J, Plodpai J, Pintavirooj C. An efficient method for segmentation step of automated white blood cells classifications. Proc IEEE Region 10 Conference TENCON; 2004.
• [112] Hegde RB, Prasad a K, Hebbar H, Singh BMK. Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybern Biomed Eng 2019;39:382–92.
• [113] Ghosh P, Bhattacharjee D, Nasipuri Ma. Blood smear analyzer for white blood cell counting: a hybrid microscopic image analyzing technique. Appl Soft Comput 2016;46:629–38.
• [114] Sudha K, Geetha P. A novel approach for segmentation and counting of overlapped leukocytes in microscopic blood images. Biocybern Biomed Eng 2020;40(2):639–48. http://dx.doi.org/10.1016/j.bbe.2020.02.005.
• [115] Shirazi SH, Umar AI, Haq NUI, Naz S, Razzak MI. Accurate microscopic red blood cell image enhancement and segmentation. Proc International Conference on Bioinformatics and Biomedical Engineering; 2015.
• [116] Benazzouz M, Baghli I, Chikh MA. Microscopic image segmentation based on pixel classification and dimensionality reduction. Int J Imaging Syst Technol 2013;23:22–8.
• [117] Haussmann G, Liedtke C-E. A region extraction approach to blood smear segmentation. Comput Vision Graph Image Process 1984;25:133–50.
• [118] Pansombut T, Wikaisuksakul S, Khongkraphan K, Phon-on A. Convolutional neural networks for recognition of lymphoblast cell images. Comput Intell Neurosci 2019. http://dx.doi.org/10.1155/2019/7519603. Article ID 7519603, 12 pp.
• [119] Matek C, Schwarz S, Spiekermann K, Marr C. Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks. Nat Mach Intell 2019;1:538–44. http://dx.doi.org/10.1038/s42256-019-0101-9.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).