TY - GEN
T1 - Automated nucleus segmentation of leukemia blast cells
T2 - 2nd International Conference on Data, Engineering and Applications, IDEA 2020
AU - Shinde, Saksha
AU - Sharma, Neeraj
AU - Bansod, Prashant
AU - Singh, Munendra
AU - Singh Tekam, Chandra Kant
N1 - Funding Information:
VI. ACKNOWLEDGEMENT : We, as authors, are thankful for support and fund obtained from Technical Education Quality Improvement Programme Phase III , under the Collaborative Research Scheme (Grant No. 1-5759733371), Ministry of Human Resource Development, India.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Leukemia detection using computer vision algorithms is a significant step in computer-assisted diagnosis for a pathologist. In order to extract the blood cells, many color space models are used for image enhancement and as the preprocessing steps. The present work compares the effect of the green, saturation, Cb and M component of RGB, HSV, YCbCr and CMY color spaces for segmentation of nucleus of blast cells in a leukemia patient's blood smear. The segmentation result of each color space for every ten images is divided into three categories i.e. only WBC segmentation, WBC with peripheral cells and all blood cell segmentation. The study demonstrates that the performance of segmentation is negatively correlated with contrast and illuminance of the input image. HSV and CMY models obtained 85% segmentation accuracy. The present study would help researchers to narrow down their selection when choosing a color space model for segmenting the nucleus of leukemia blast cells.
AB - Leukemia detection using computer vision algorithms is a significant step in computer-assisted diagnosis for a pathologist. In order to extract the blood cells, many color space models are used for image enhancement and as the preprocessing steps. The present work compares the effect of the green, saturation, Cb and M component of RGB, HSV, YCbCr and CMY color spaces for segmentation of nucleus of blast cells in a leukemia patient's blood smear. The segmentation result of each color space for every ten images is divided into three categories i.e. only WBC segmentation, WBC with peripheral cells and all blood cell segmentation. The study demonstrates that the performance of segmentation is negatively correlated with contrast and illuminance of the input image. HSV and CMY models obtained 85% segmentation accuracy. The present study would help researchers to narrow down their selection when choosing a color space model for segmenting the nucleus of leukemia blast cells.
UR - https://www.scopus.com/pages/publications/85091439726
UR - https://www.scopus.com/pages/publications/85091439726#tab=citedBy
U2 - 10.1109/IDEA49133.2020.9170721
DO - 10.1109/IDEA49133.2020.9170721
M3 - Conference contribution
AN - SCOPUS:85091439726
T3 - 2nd International Conference on Data, Engineering and Applications, IDEA 2020
BT - 2nd International Conference on Data, Engineering and Applications, IDEA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 February 2020 through 29 February 2020
ER -