Identification of Giemsa Staind of Malaria Using K-Means Clustering Segmentation Technique

Malaria is disease the most common in the world. The disease is caused by mosquito bites anywhere. The identification process will take some time to deliver maximum results. One of the techniques used to identification image of blood cell is segmentation, the technique used in this study is a K-Means. K-means clustering algorithm can be combined with segmentation technique to obtain the identification of malaria virus. In on this study K-Means clustering segmentation techniques capable of providing identification automation with Giemsa staining of malaria, and the results of blood that is infected will be directly identified.

[1]  Gabriele Steidl,et al.  A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data , 2012, Pattern Recognit..

[2]  V. Agarwal,et al.  Detection of malarial parasite by blood smear examination and antigen detection: A comparative study , 2013 .

[3]  Dongjian He,et al.  Image segmentation algorithm for disease detection of wheat leaves , 2014, Proceedings of the 2014 International Conference on Advanced Mechatronic Systems.

[4]  Qiang Chen,et al.  Fuzzy c-means clustering with weighted image patch for image segmentation , 2012, Appl. Soft Comput..

[5]  Ibrahim Azad,et al.  Image Processing for Skin Cancer Features Extraction , 2013 .

[6]  T. S. Awolola,et al.  Species Composition and Role of Anopheles Mosquitoes in Malaria Transmission Along Badagry Axis of Lagos Lagoon, Lagos, Nigeria , 2010 .

[7]  Fumihiko Mori,et al.  Region Segmentation and Object Extraction Based on Virtual Edge and Global Features , 2012, ACCV Workshops.

[8]  Jing Hu,et al.  Image Segmentation Method for Crop Nutrient Deficiency Based on Fuzzy C-Means Clustering Algorithm , 2012, Intell. Autom. Soft Comput..

[9]  Yaming Wang,et al.  Segmentation of rice disease spots based on improved BPNN , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[10]  M. Y. Mashor,et al.  A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[11]  Li Cheng-hua,et al.  Color image segmentation method based on statistical pattern recognition for plant disease diagnose , 2004 .

[12]  M. Y. Mashor,et al.  Malaria parasite detection with histogram color space method in Giemsa-stained blood cell images , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[13]  M. Y. Mashor,et al.  Automated segmentation procedure for Ziehl-Neelsen stained tissue slide images , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[14]  Tania S. Douglas,et al.  Automated detection of malaria in Giemsa-stained thin blood smears , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Nadeem Akhtar,et al.  K-mean algorithm for Image Segmentation using Neutrosophy , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[16]  Yambem Jina Chanu,et al.  Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm , 2015 .

[17]  Max Mignotte A de-texturing and spatially constrained K-means approach for image segmentation , 2011, Pattern Recognit. Lett..