IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

Asaad Babker, Vyacheslav Lyashenko

Abstract


Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure.

 


Full Text:

PDF

References


Li H, Colin S. Characteristic peripheral blood smear findings in disorders of cobalamin metabolism. Blood 2016;128(21): 2584-2584.

Chari PS, Prasad S. Pilot Study on the Performance of a New System for Image Based Analysis of Peripheral Blood Smears on Normal Samples. Indian Journal of Hematology and Blood Transfusion. 2017: 1-7.

Yamamoto A, Kambara Y, Urata T, Kuroi T, Masunari T, Sezaki N, Kiguchi T. Mean Corpuscular Volume Evaluation for Differential Diagnosis of Myelodysplastic Syndrome and Megaloblastic Anemia: A Study of 130 Patients. Clinical Lymphoma, Myeloma and Leukemia. 2017;17: S346-S347.

Bizzaro N, Antico A. Diagnosis and classification of pernicious anemia. Autoimmunity reviews. 2014; 13(4): 565-568.

Putzu L, Caocci G, Di Ruberto C. Leucocyte classification for leukaemia detection using image processing techniques. Artificial intelligence in medicine. 2014; 62(3): 179-191.

Tomari R, Zakaria WNW, Jamil MMA, Nor FM, Fuad NFN. Computer aided system for red blood cell classification in blood smear image. Procedia Computer Science. 2014;42:206-213.

Lyashenko V, Babker AMAA, Kobylin OA. The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Med J. 2016; 41(3): 453-463.

Lyashenko V, Matarneh R, Kobylin O, Putyatin Y. Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal of Advance Research in Computer Science and Management Studies. 2016;4: 85-94.

Dey N, Ashour AS, Ashour AS, Singh A. Digital analysis of microscopic images in medicine. Journal of Advanced Microscopy Research. 2015; 10: 1-13.

Xiong W, Ong SH, Lim JH, Cheng J, Gu Y Blood Smear Analysis, Malaria Infection Detection, And Grading From Blood Cell Images. Biomedical Image Understanding, Methods and Applications 2015:275-324.

Kobylin O, Lyashenko V. Comparison of standard image edge detection techniques and of method based on wavelet transform. International Journal of Advanced Research.2014; 2(8): 572-580.

Lyashenko V, Babker AM. Using of Color Model and Contrast Variation in Wavelet Ideology for Study Megaloblastic Anemia Cells. Open Journal of Blood Diseases. 2017; 7(03): 86-102.

Saha M, Agarwal S, Arun I, Ahmed R, Chatterjee S, Mitra P, Chakraborty C. Histogram Based Thresholding for Automated Nucleus Segmentation Using Breast Imprint Cytology. Advancements of Medical Electronics. 2015: 49-57.

Mahendran G, Babu R, Sivakumar D. Automatic segmentation and classification of pap smear cells. International Journal of Management, IT and Engineering. 2014; 4(5): 100-108.

Ensink E, Sinha J, Sinha A, Tang H, Calderone HM, Hostetter G, Haab BB. Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data. Analytical chemistry 2015;87(19): 9715-9721.

Malviya R, Karri SPK, Chatterjee J, Manjunatha M, Ray AK. Computer assisted cervical cytological nucleus localization. TENCON 2012-2012 IEEE Region 10 Conference. IEEE 2012: 1-5.

Singh S, Gupta R. Identification of components of fibroadenoma in cytology preparations using texture analysis: a morphometric study. Cytopathology 2012; 23(3): 187-191.

Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. Biomedical Engineering. 2010; 57(4): 841-852.




DOI: https://doi.org/10.31878/ijcbr.2018.43.01

Article Statistics:

Abstract 45 PDF 15

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.