White blood cell recognition via geometric features and naïve bays classifier

  • Authors

    • Hasan A. Kazum
    • Faisel G. Mohammed
    https://doi.org/10.14419/ijet.v7i4.21747

  • Abstract

    Blood assessments are of the maximum crucial and frequently asked medical examinations. A manual microscopic evaluation must be done while a blood pattern is suspicious of abnormality. This manual technique is tedious, time ingesting and subjective. Automating microscopic blood type is appropriate to assist the pathologists to hurry-up and induce the consequences accuracy.

    Segmentation is the primary and common step in computerized WBCs category. On this paper, have been presented a powerful method for automated WBCs nuclei segmentation. The technique is based on gray scale contrast enhancement and then using Otsu thresholding tech-nique to segment WBCs. There are four features have found to extract the data from the segmented image. These features are (Area, Perim-eter, diameter, Circularity. Then these data was classified using Naive Bayes classifier under weka program. The approach is examined on 260 blood pictures. The class overall performance is quantitatively evaluated at the take a look at set to be 97,1 %. This overall performance is excessive in comparison to other related work done at the identical dataset.

  • References

    1. [1] Razzak MI, Naz S, Zaib A: Deep Learning for Medical Image Processing: Overview, Challenges and the Future. In: Classification in BioApps. Springer; 2018: 323-350. https://doi.org/10.1007/978-3-319-65981-7_12.

      [2] Shafique S, Tehsin S: Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia. Computational and mathematical methods in medicine 2018, 2018.

      [3] Liu Z, Liu J, Xiao X, Yuan H, Li X, and Chang J, Zheng C: Segmentation of white blood cells through nucleus mark watershed operations and mean shift clustering. Sensors 2015, 15(9):22561-22586.

      [4] Habibzadeh M, Krzyżak A, Fevens T: White blood cell differential counts using convolutional neural networks for low-resolution images. In: International Conference on Artificial Intelligence and Soft Computing: 2013. Springer: 263-274. https://doi.org/10.1007/978-3-642-38610-7_25.

      [5] Alférez Baquero ES: Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images. 2015.

      [6] Dragozi E, Gitas IZ, Stavrakoudis DG, Theocharis JB: Burned area mapping using support vector machines and the FuzCoC feature selection method on VHR IKONOS imagery. Remote Sensing 2014, 6(12):12005-12036. https://doi.org/10.3390/rs61212005.

      [7] Ibraheem NA, Khan RZ, and Hasan MM: Comparative study of skin color based segmentation techniques. International Journal of Applied Information Systems (IJAIS) 2013, 5(10).

      [8] Cunningham SJ, Holmes G: Developing innovative applications in agriculture using data mining. 2001.

      [9] Sathpathi S, Mohanty AK, Satpathi P, Mishra SK, Behera PK, and Patel G, Dondorp AM: Comparing Leishman and Giemsa staining for the assessment of peripheral blood smear preparations in a malaria-endemic region in India. Malaria journal 2014, 13(1):512. https://doi.org/10.1186/1475-2875-13-512.

      [10] Prodanov D, Verstreken K: Automated segmentation and morphometry of cell and tissue structures. Selected algorithms in imageJ. In: Molecular Imaging. InTech; 2012. https://doi.org/10.5772/36729.

      [11] Gautam A, Singh P, Raman B, Bhadauria H: Automatic classification of leukocytes using morphological features and naïve Bayes classifier. In: Region 10 Conference (TENCON), 2016 IEEE: 2016. IEEE: 1023-1027.

      [12] Yang X, Shen X, Long J, Chen H: An improved median-based Otsu image thresholding algorithm. Aasri Procedia 2012, 3:468-473. https://doi.org/10.1016/j.aasri.2012.11.074.

      [13] Gautam A, Bhadauria H: Classification of white blood cells based on morphological features. In: Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on: 2014. IEEE: 2363-2368.

      [14] Gonzales R, Woods E: Digital Image Processing, 3, d edition. In.: Prentice-Hall; 2007.

      [15] Barbedo JGA: Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2013, 2(1):660. https://doi.org/10.1186/2193-1801-2-660.

      [16] Su M-C, Cheng C-Y, Wang P-C: A neural-network-based approach to white blood cell classification. The scientific world journal 2014, 2014.

      [17] Raschka S: Naive bayes and text classification i-introduction and theory. arXiv preprint arXiv:14105329 2014.

      [18] Amancio DR, Comin CH, Casanova D, Travieso G, Bruno OM, Rodrigues FA, da Fontoura Costa L: A systematic comparison of supervised classifiers. PloS one 2014, 9(4):e94137. https://doi.org/10.1371/journal.pone.0094137.

      [19] Wu H, Phang TH, Liu B, Li X: A refinement approach to handling model misfit in text categorization. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining: 2002. ACM: 207-216. https://doi.org/10.1145/775047.775078.

      [20] Kang H-W, Kang H-B: Prediction of crime occurrence from multi-modal data using deep learning. PloS one 2017, 12(4):e0176244. https://doi.org/10.1371/journal.pone.0176244.

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  • How to Cite

    Kazum, H. A., & Mohammed, F. G. (2018). White blood cell recognition via geometric features and naïve bays classifier. International Journal of Engineering & Technology, 7(4), 3642-3646. https://doi.org/10.14419/ijet.v7i4.21747

    Received date: 2018-11-26

    Accepted date: 2018-11-26