Dengue Fever Classification Based on Grey Level Co-occurrence Matrix Feature

White blood cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body’s usual healing process. White blood cells have potential to divide themselves (through mitosis) to produce more White Blood Cells. Any disease that results in cellular and tissue destruction can potentially be treated by WBC cells. Detection of WBC cells has become an important part in modern medicine to diagnose any disease at its prior onset. But due to their characteristics to change their shape, size and colour at different intervals of time it becomes quite difficult to detect and segment them, as this research is going on to detect WBC cells by using the most efficient algorithm among all that have been studied in the literature survey.

[1]  Mohd Razali Md Tomari,et al.  White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods , 2018 .

[2]  H. S. Bhadauria,et al.  White blood nucleus extraction using K-Mean clustering and mathematical morphing , 2014, 2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence).

[3]  P. Viswanathan,et al.  Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation , 2015 .

[4]  Jennie Malboeuf Algorithm , 1994, Neurology.

[5]  Wichian Premchaiswadi,et al.  Image processing for detection of dengue virus based on WBC classification and decision tree , 2015, 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015).

[6]  Flávio H. D. Araújo,et al.  Unsupervised Leukemia Cells Segmentation Based on Multi-space Color Channels , 2016, 2016 IEEE International Symposium on Multimedia (ISM).

[7]  S. P. Narote,et al.  Blood cell segmentation from microscopic blood images , 2015, 2015 International Conference on Information Processing (ICIP).

[8]  Uzzal Kumar Prodhan,et al.  Segmentation of White Blood Cells Using Fuzzy C Means Segmentation Algorithm , 2014 .

[9]  J. Poornima,et al.  Detection of Dengue Fever with Platelets Count using Image Processing Techniques , 2016 .

[10]  Sanaullah Khan,et al.  An Accurate and Cost Effective Approach to Blood Cell Count , 2012 .

[11]  R. Regan,et al.  The detection of , 1973 .

[12]  Ardeshir Talebi,et al.  Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm , 2017, Journal of medical signals and sensors.

[13]  Xiaomei Li,et al.  Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering , 2015, Sensors.