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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.1, Jan. 2018

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Full Text (PDF, 574KB), PP.30-35


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Author(s)

Abdellatif BOUZID-DAHO, Mohamed BOUGHAZI

Index Terms

Abnormal (cancerous) blood cells;k-means; microscopic medical images; segmentation;classification

Abstract

The aim of our work is to obtain a maximum rate of recognition of abnormal (cancerous) blood cells. We propose the development of a system based on k-means methods, after an RGB channel decomposition by applying the algorithm which can segment our microscopic medical images. It turns out that the proposed system shows better segmentation and classification for the identification and detection of leukemia. The experimental results obtained are very encouraging, which helps hematologists to monitor the evolution of cancerous blood cells and make a good diagnosis.

Cite This Paper

Abdellatif BOUZID-DAHO, Mohamed BOUGHAZI," Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.1, pp. 30-35, 2018.DOI: 10.5815/ijigsp.2018.01.04

Reference

[1]P. Purohit and R. Joshi. ‘’A New Efficient Approach towards k-means Clustering Algorithm’’, In International Journal of Computer Applications, Vol.65, N°.11, 2013.

[2]A. Jose, S. Ravi and M. Sambath. ‘’Brain Tumor Segmentation using k–means Clustering and Fuzzy C -means Algorithm and its Area Calculation’’, In International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, N°.2, 2014.

[3]S. Mishra, and M. Panda. ‘’A Histogram-based Classification of Image Database Using Scale Invariant Features’’, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.9, N°.6, pp.55-64, 2017. 

[4]A. Bouzid-Daho, and all. ‘’Algorithmic Processing to Aid Leukemia Detection’’, In Medical Technologies Journal, Vol.1, N°.1, pp.10-11, 2017.

[5]K. Bhima, and A. Jagan. ‘’An Improved Method for Automatic Segmentation and Accurate Detection of Brain Tumor in Multimodal MRI’’, In International Journal of Image, Graphics and Signal Processing, Vol.9, N°.5, pp.1-8, 2017.

[6]T. Kalaiselvi and P. Nagaraja, “A Rapid Automatic Brain Tumor Detection Method for MRI Images using Modified Minimum Error Thresholding Technique” International Journal of Imaging Systems and Technology, Vol.25, N°.1, pp.77–85, 2015.

[7]S. Selvaraj, and B.R Kanakaraj, ‘‘K-Means Clustering Based Segmentation of Lymphocytic Nuclei for Acute Lymphocytic Leukemia Detection’, International Journal of Applied Engineering Research, Vol.9, N°.21, pp.11423-11432, 2014.

[8]C. Di Ruberto and L. Putzu, “Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold,” in Tenth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS’14), pp. 57–64. 2014

[9]Q. Wang, L. Chang, M. Zhou, M. and Q, L. ‘‘A spectral and morphologic method for white blood cell classification’’, ELSEVIER: Optics & Laser Technology, Vol.84, pp.144-148, 2016.

[10]L. A. Bhavnani, U. K. Jaliya and M J. Joshi. ‘’Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images’’, In International Journal of Image, Graphics and Signal Processing, Vol.8, N°.11. pp.32-40, 2016.

[11]A. Bouzid-Daho, and all. ‘’SEGMENTATION OF ABNORMAL BLOOD CELLS TO AID LEUKEMIA DETECTION’’, In Acta HealthMedica Journal, Vol. 1, N°. 4, pp. 88-92, 2016.

[12]F. Mashiat, and J. Sharma, J. ‘‘Identification and classification of acute leukemia using neural network.’’ In Medical Imaging, m-Health and Emerging Communication Systems, International Conference on (MedCom) IEEE, pp.142-145, 2014.

[13]A. Bouzid-Daho, and all. ‘’Textural Analysis of Bio-Images for Aid in the Detection of Abnormal Blood Cells’’, In International Journal of Biomedical Engineering and Technology, Vol.25, N°.1, pp.1-13, 2017.

[14]X. Wu, and all. ‘‘Differentiation of Diffuse Large B-cell Lymphoma From Follicular Lymphoma Using Texture Analysis on Conventional MR Images at 3.0 Tesla’’, Academic Radiology, ELSEVIER, Vol.23, N°.6, pp.696-703, 2016.

[15]M. D. Joshi, A. H. Karode, and S. R. Suralkar. ‘’White Blood Cells Segmentation and Classification to Detect Acute Leukemia’’, In International Journal of Emerging Trends & Technology in Computer Science, Vol.2, N°.3, pp.147-151, 2013.

[16]http://hematocell.univ-angers.fr/index.php/banque-dimages. Cons: 16/03/2017.