Research article

Red and white blood cell classification using Artificial Neural Networks

  • Received: 25 July 2018 Accepted: 18 September 2018 Published: 08 October 2018
  • Blood cell classification is a recent topic for scientists working on the diagnosis of blood cell related illnesses. As the number of computer vision (CV) applications is increasing to improve quality of human life, it spreads in the areas of autonomous drive, surveillance, robotic applications, telecommunications and etc. The number of CV applications also increases in the medical sector due the decreasing value of doctors per patient ratio (DPPR) in urban and suburban areas. A doctor working in such areas sometimes would have to interpret thousands of patients’ test results in a day. This condition would result disadvantages such as false diagnosis on patients and break on working motivations for doctors. Some of the tests would probably be interpreted using an application developed by Artificial Neural Networks (ANN). Tests related to blood cells are examined for the patients as a starting point of diagnosis and information obtained about their abnormalities give doctors a preliminary idea about the illnesses. This article issues generation of a CV application that would be used as an assistant of doctors who have domain expertise. The article issues segmentation of blood cells, classification of red and white blood cells containing 6 types such as erythrocyte, lymphocyte, platelets, neutrophil, monocytes and eosinophils using the segmentation results. It also discusses about a method for detection of abnormalities on red blood cells (erythrocyte).

    Citation: Simge Çelebi, Mert Burkay Çöteli. Red and white blood cell classification using Artificial Neural Networks[J]. AIMS Bioengineering, 2018, 5(3): 179-191. doi: 10.3934/bioeng.2018.3.179

    Related Papers:

  • Blood cell classification is a recent topic for scientists working on the diagnosis of blood cell related illnesses. As the number of computer vision (CV) applications is increasing to improve quality of human life, it spreads in the areas of autonomous drive, surveillance, robotic applications, telecommunications and etc. The number of CV applications also increases in the medical sector due the decreasing value of doctors per patient ratio (DPPR) in urban and suburban areas. A doctor working in such areas sometimes would have to interpret thousands of patients’ test results in a day. This condition would result disadvantages such as false diagnosis on patients and break on working motivations for doctors. Some of the tests would probably be interpreted using an application developed by Artificial Neural Networks (ANN). Tests related to blood cells are examined for the patients as a starting point of diagnosis and information obtained about their abnormalities give doctors a preliminary idea about the illnesses. This article issues generation of a CV application that would be used as an assistant of doctors who have domain expertise. The article issues segmentation of blood cells, classification of red and white blood cells containing 6 types such as erythrocyte, lymphocyte, platelets, neutrophil, monocytes and eosinophils using the segmentation results. It also discusses about a method for detection of abnormalities on red blood cells (erythrocyte).


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    [1] Shapiro MF, Sheldon G (1987) The Complete Blood Count and Leukocyte Differential Count: An Approach to Their Rational Application. J Emerg Med 106: 65–74.
    [2] Lynch E C. (1990) Peripheral blood smear. Butterworths, Boston: Pubmed, 90: 1373–1377.
    [3] Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109. doi: 10.1093/biomet/57.1.97
    [4] Ng HP, Ong SH, Foong KWC, et al. (2006) Medical image segmentation using k-means clustering and improved watershed algorithm. IEEE Southwest Symp Image Anal Interpret 106: 61–65.
    [5] Kurita T, Otsu N, Abdelmalek N (1992) Maximum likelihood thresholding based on population mixture models. Pattern Recogn 25: 1231–1240. doi: 10.1016/0031-3203(92)90024-D
    [6] Danielsson PE (1980) Euclidean distance mapping. Comput GraphImage Process 14: 227–248. doi: 10.1016/0146-664X(80)90054-4
    [7] Sobel I (1990) An isotropic 3 × 3 image gradient operator. Mach vision three dimensional scenes, 376–379.
    [8] Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. IEEE Int Conf Computl Intelli Meas Syst Appl CIMSA. 25: 96–101
    [9] Sadeghian F, Seman Z, Ramli AR, et al. (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 11: 196. doi: 10.1007/s12575-009-9011-2
    [10] Jesus A, Georges F (2003) Automated detection of working area of peripheral blood smears using mathematical morphology. Anal Cell pathol 25: 37–49. doi: 10.1155/2003/642562
    [11] Goswami R, Pi D, Pal J, et al. (2015) Performance evaluation of a dynamic telepathology system (Panoptiq(™)) in the morphologic assessment of peripheral blood film abnormalities. Int J Lab hematol 37: 365–371. doi: 10.1111/ijlh.12294
    [12] D'Ambrosio MV, Bakalar M, Bennuru S, et al. (2015) Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci Transl Med 7: 286re4.
    [13] Manik S, Saini LM, Vadera N (2017) Counting and classification of White blood cell using Artificial Neural Network (ANN). IEEE Int Con Power Electron Intell Control Energy Syst 2017:1–5
    [14] Jia YQ, Shelhamer E, Donahue J, et al.(2014) Caffe: Convolutional architecture for fast feature embedding. Proc 22nd ACM Int Conf Multimedia, 675–678
    [15] Das DK, Maiti AK, Chakraborty C (2015) Automated system for characterization and classification of malaria infected stages using light microscopic images of thin blood smears. J Microsc-Oxford 257: 238–252.
    [16] Automatic Peripheral Blood Smear and Slide Scanner Device. Available from: http://www.mantiscope.com
    [17] Devi S, Singha J, Sharma M, et al. (2016) Erythrocyte segmentation for quantification in microscopic images of thin blood smears. J Intell Fuzzy Syst 4: 2847–2856.
    [18] Lee H, Chen YPP (2014) Cell morphology based classification for red cells in blood smear images. Pattern Recogn Lett 49: 155–161. doi: 10.1016/j.patrec.2014.06.010
    [19] Amin MM, Kermani S, Talebi A, et al. (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using K-means clustering and support vector machine classifier. J Med Signal Sensor 5: 49.
    [20] Li Y, Zhu R, Mi L, et al. (2016) Segmentation of white blood cell from acute Lymphoblastic Leukemia images using dual-threshold method. ComputMathMethod M 2016: 9514707.
    [21] Linder N,Tukki R, Walliander M, et al. (2014) A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PLos One 9: e104855.
    [22] Zhu C, Zheng Y, Luu K, et al. (2016) CMS-RCNN: contextual multi-scale region-based CNN for unconstrained face detection. In Bhanu B, Kumar A, Deep Learning for Biometrics, 3 Eds., Switzerland: Springer , 57–79.
    [23] Beucher S, Mathmatique CDM (1991) The watershed transformation applied to image segmentation. Scanning Microsc Suppl 6: 299–314
    [24] Dollar P, Wojek C, Schiele B, et al. (2012) Pedestrian detection: An evaluation of the state of the art. IEEE T Pattern Anal 34: 743. doi: 10.1109/TPAMI.2011.155
    [25] Redmon J, Divvala S, Girshick R, et al. (2016) You only look once: Unified, real-time object detection. Comput Vision Pattern Recognit 2016: 779–788.
    [26] He K, Zhang X, Ren S, et al. (2016) Deep residual learning for image recognition. IEEE conference on Compute Vison and Pattern Recogn, 770–778.
    [27] Govind D, Lutnick B, Tomaszewski JE, et al. (2018). Automated erythrocyte detection and classification from whole slide images. J Med Imag 5: 027501.
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