A Novel and More Efficient Approach for Automatic Diagnosis of Acute Lymphoblastic Leukemic Cells based on Combining Geometrical and Statistical Features of Blood Cells

Document Type : Original Article (s)

Authors

1 MSc Student, Student Research Committee, Department of Bioelectric and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 Associate Professor, Department of Bioelectric and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 Associate Professor, Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Acute lymphoblastic leukemia (ALL) is one of the most common types of leukemia among children. Due to the large number of clinical laboratories, in those with no expert pathologist for diagnosis of leukemia, software can be a useful tool for diagnostic purposes. The aim of this study was to create an automatic detector to help diagnosis process.Methods: Using automatic segmentation algorithm, the nucleus of blast and lymphocyte cells were separated from existing images. As the chaotic characteristic caused significant difference in edges and string patterns, three geometrical, statistical, and chaotic features were derived from cells. In order to diagnosis and classification, support vector machine algorithm was used and the accuracy of classification was investigated using receiver characteristic operating curves (ROC).Findings: This study was conducted on 312 microscopic images including blast and lymphocyte cells. There was a specificity of more than 92% and an accuracy of more than 93% in six cell groups. In addition, checking out the area under the ROC curve represented more than 91% efficiency for suggested method.Conclusion: The findings indicate the effectiveness of these features in classification. Differentiation of blast and lymphocyte cells, that are different only in size of chromatin, and also uneven shape of lymphocyte cytoplasm, are of the advantages of using chaotic features.

Keywords


  1. Bain BJ. A beginner's guide to blood cells. 2nd ed. Hoboken, NJ: Wiley-Blackwell; 2004. p. 64-5.
  2. Hall JE. Guyton and Hall textbook of medical physiology. 13th ed. Philadelphia, PA: Saunders; 2015.
  3. Haworth C, Heppleston AD, Morris Jones PH, Campbell RH, Evans DI, Palmer MK. Routine bone marrow examination in the management of acute lymphoblastic leukaemia of childhood. J Clin Pathol 1981; 34(5): 483-5.
  4. Moradi Amin M, Kermani S, Talebi A, Oghli MG. Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. J Med Signals Sens 2015; 5(1): 49-58.
  5. Moradi Amin 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 Tech 2016; 79(10): 908-16.
  6. Moradi Amin M, Samadzadehaghdam N, Kermani S, Talebi A. Enhanced recognition of acute lymphoblastic leukemia cells in microscopic images based on feature reduction using principle component analysis. Frontiers in Biomedical Technologies 2015; 2(3): 128-36.
  7. Ghane N, Vard A, Talebi A, Nematollahy P. Classification of effective cells in diagnosis of chronic myeloid leukemia (CML) using semi-automatic image processing of microscopic images. J Isfahan Med Sch 2017; 34(405): 1304-10. [In Persian].
  8. Saeedizadeh Z, Mehri DA, Talebi A, Rabbani H, Sarrafzadeh O, Vard A. Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier. J Microsc 2016; 261(1): 46-56.
  9. Mohapatra S, Patra D, Satpathy S. An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput Appl 2014; 24(7): 1887-904.
  10. Al-Kadi OS. A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours. Proceedings of the 16th IEEE International Conference on Image Processing (ICIP) 2009; 2009 Nov 7-10; Cairo, Egypt. p. 4177-80.
  11. Napolitano A, Ungania S, Cannata V. Fractal Dimension estimation methods for biomedical images. INTECH Open Access Publisher; 2012.
  12. Al-Kadi OS, Watson D. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Trans Biomed Eng 2008; 55(7): 1822-30.
  13. Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 1979; 9(1): 62-6.
  14. Suykens JAK, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Process Lett 1999; 9(3): 293-300.
  15. Fawcett T. ROC graphs: Notes and practical considerations for researchers. ReCALL 2004; 31(HPL-2003-4): 1-38.