Preliminary process in blast cell morphology identification based on image segmentation methods

The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

[1]  Cecilia Di Ruberto,et al.  White Blood Cells Counting Via Vector Field Convolution Nuclei Segmentation , 2018, VISIGRAPP.

[2]  Omid Sarrafzadeh,et al.  The Best Texture Features for Leukocytes Recognition , 2017, Journal of medical signals and sensors.

[3]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[4]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[5]  Anubha Gupta,et al.  PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma , 2018, PloS one.

[6]  Retno Supriyanti,et al.  Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image , 2018 .

[7]  Abdellah Madani,et al.  Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network , 2018 .

[8]  Jaroonrut Prinyakupt,et al.  Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers , 2015, BioMedical Engineering OnLine.

[9]  Yogi Ramadhani,et al.  Influence of camera types in histogram distribution on morphological identification of myeloblast cell based image segmentation , 2019 .

[10]  Yiping Cao,et al.  A robust automatic leukocyte recognition method based on island-clustering texture , 2016 .

[11]  Weixing Wang,et al.  A robust leukocyte recognition method based on multi-scale regional growth and mean-shift clustering , 2018 .

[12]  Organización Mundial de la Salud.  World Health Statistics 2008 , 2018 .

[13]  Kundan Kumar,et al.  Fast leukocyte image segmentation using shadowed sets , 2012, Int. J. Comput. Biol. Drug Des..

[14]  Duraiswamy Umamaheswari,et al.  A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-Knn Classifier , 2018, J. Comput. Inf. Technol..

[16]  M. Zimic,et al.  An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images , 2019, International Journal of Electrical and Computer Engineering (IJECE).

[17]  Mazin Z. Othman,et al.  Segmentation and Feature Extraction of Lymphocytes WBC using Microscopic Images , 2014 .

[18]  Mei Zhou,et al.  Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology , 2017 .

[19]  Ahmed H. Kandil,et al.  A decision support system for Acute Leukaemia classification based on digital microscopic images , 2017, Alexandria Engineering Journal.

[20]  Retno Supriyanti,et al.  Technology supporting health services for rural areas based on image processing , 2019, Journal of Physics: Conference Series.

[21]  H. S. Bhadauria,et al.  Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images , 2015 .

[22]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.