Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia
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 overlappe...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| 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 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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. |
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