Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

• Main Authors: Siew Ming, Kiu, Yin Chai, Wang
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/40874/1/Geometric%20Feature%20Extraction%20-%20Copy.pdf; http://ir.unimas.my/id/eprint/40874/; https://www.mdpi.com/2673-7426/2/2/15; https://doi.org/10.3390/biomedinformatics2020015
• Institution: Universiti Malaysia Sarawak

This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. Geometric features of blood cells are proposed to identify and classify overlapping cells into groups based on different overlapping degrees and the number of overlapped cells. In the proposed method, the percentage of average accuracy for identifying overlapping cells reached 98 percent. The proposed method can segment white blood cells from the overlapping cells with an overlapping degree of 70 percent. Improved Watershed Algorithm successfully increased 36.89 percent of accuracy in WBC segmentation. It reduced 46.12 percent of the over-segmentation problem. Tests of cell counting are conducted in the two stages, which are before and after the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31 percent, the average percentage of WBC counting is 84.8 percent, and the average percentage of RBC counting is 60.55 percent. The proposed method is efficient in identifying and classifying overlapping cells for increasing the accuracy of cell counting.