Erythrocyte segmentation for quantification in microscopic images of thin blood smears

• Published in: Journal of intelligent & fuzzy systems 2017-01, Vol.32 (4), p.2847-2856
• Main Authors: Devi, Salam Shuleenda, Singha, Joyeeta, Sharma, Manish, Laskar, Rabul Hussain
• Format: Article
• Language: English
• Subjects: Aspect ratio; Bayesian analysis; Erythrocytes; Filtration; Image segmentation; Malaria; Model accuracy; Preprocessing; Separation
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-169227

Manual analyzing and interpreting of the microscopic images of thin blood smears for diagnosis of the malaria is a tedious and challenging task. This paper aims to develop a computer assisted system for quantification of erythrocytes in microscopic images of thin blood smears. The proposed method consists of preprocessing, segmentation, morphological filtering, cell separation and clump cell segmentation. The major issues, required to be addressed to enhance the performance of the system are cell separation (i.e. isolated and clump erythrocytes classification) and clump cell segmentation. The geometric features such as cell area, compactness ratio and aspect ratio have been used to define the feature set. Further, the performance of the system in classifying the isolated and clump erythrocytes is evaluated for the different classifiers such as Naive Bayes, k-NN and SVM. Moreover, the clump erythrocytes are segmented using marker controlled watershed with h-minima as internal marker. Based on the experimental results, it may be concluded that the proposed model provides satisfactory results with an accuracy of 98.02% in comparison to the state of art method.