TY - GEN
T1 - Segmentation of white blood cells using image segmentation Algorithms
AU - Kumar, Puranam Revanth
AU - Sarkar, Achyuth
AU - Mohanty, Sachi Nandan
AU - Kumar, P. Pavan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/14
Y1 - 2020/10/14
N2 - Segmentation of images plays a key role in Image Processing as it simplifies further processing by separating a broad image into many parts. White Blood Cells (WBC) microscopic images allow haematologists to predict vulnerability to several diseases. Our intension is to identify Nucleus of White Blood cells. Colour is a significant reference point for discerning segmented WBCs from microscopic images. Results reveal that in identifying the nucleus of various types of WBC: including Neutrophils, Lymphocytes, Eosinophils, Monocytes, Basophils. In this study, we will evaluate the efficiency of the Edge detection Algorithm (log Canny) methods, k-Means Algorithm, Linear Transformed Image, Color-based technique, and will compare the results with the original image. This will allow haematologists for clear identification of WBC. Analysis of the results using Algorithms shows that Edge detection and k-means based segmentation is the most suitable approach for segmenting WBC cells.
AB - Segmentation of images plays a key role in Image Processing as it simplifies further processing by separating a broad image into many parts. White Blood Cells (WBC) microscopic images allow haematologists to predict vulnerability to several diseases. Our intension is to identify Nucleus of White Blood cells. Colour is a significant reference point for discerning segmented WBCs from microscopic images. Results reveal that in identifying the nucleus of various types of WBC: including Neutrophils, Lymphocytes, Eosinophils, Monocytes, Basophils. In this study, we will evaluate the efficiency of the Edge detection Algorithm (log Canny) methods, k-Means Algorithm, Linear Transformed Image, Color-based technique, and will compare the results with the original image. This will allow haematologists for clear identification of WBC. Analysis of the results using Algorithms shows that Edge detection and k-means based segmentation is the most suitable approach for segmenting WBC cells.
UR - http://www.scopus.com/inward/record.url?scp=85098864367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098864367&partnerID=8YFLogxK
U2 - 10.1109/ICCCS49678.2020.9277312
DO - 10.1109/ICCCS49678.2020.9277312
M3 - Conference contribution
AN - SCOPUS:85098864367
T3 - Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020
BT - Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing, Communication and Security, ICCCS 2020
Y2 - 14 October 2020 through 16 October 2020
ER -