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Detection of Malaria Parasite in Giemsa Blood Sample Using Image Processing


Affiliations
1 Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh
 

Malaria is one of the deadliest diseases ever exists in this planet. Automated evaluation process can notably decrease the time needed for diagnosis of the disease. This will result in early onset of treatment saving many lives. As it poses a serious global health problem, we approached to develop a model to detect malaria parasite accurately from giemsa blood sample with the hope of reducing death rate because of malaria. In this work, we developed a model by using color based pixel discrimination technique and Segmentation operation to identify malaria parasites from thin smear blood images. Various segmentation techniques like watershed segmentation, HSV segmentation have been used in this method to decrease the false result in the area of malaria detection. We believe that, our malaria parasite detection method will be helpful wherever it is difficult to find the expert in microscopic analysis of blood report and also limits the human error while detecting the presence of parasites in the blood sample.

Keywords

Malaria, HSV Segmentation, Watershed Segmentation, Giemsa Blood Sample, RBC.
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Abstract Views: 440

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  • Detection of Malaria Parasite in Giemsa Blood Sample Using Image Processing

Abstract Views: 440  |  PDF Views: 202

Authors

Kishor Roy
Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh
Shayla Sharmin
Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh
Rahma Bintey Mufiz Mukta
Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh
Anik Sen
Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh

Abstract


Malaria is one of the deadliest diseases ever exists in this planet. Automated evaluation process can notably decrease the time needed for diagnosis of the disease. This will result in early onset of treatment saving many lives. As it poses a serious global health problem, we approached to develop a model to detect malaria parasite accurately from giemsa blood sample with the hope of reducing death rate because of malaria. In this work, we developed a model by using color based pixel discrimination technique and Segmentation operation to identify malaria parasites from thin smear blood images. Various segmentation techniques like watershed segmentation, HSV segmentation have been used in this method to decrease the false result in the area of malaria detection. We believe that, our malaria parasite detection method will be helpful wherever it is difficult to find the expert in microscopic analysis of blood report and also limits the human error while detecting the presence of parasites in the blood sample.

Keywords


Malaria, HSV Segmentation, Watershed Segmentation, Giemsa Blood Sample, RBC.

References