Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database Benchmark
2.2. Geometric Feature Extraction
3. Results and Discussion
3.1. Experiment I: Overlapping Cells Identification
3.2. Experiment II: Overlapping Cells Classification by Overlapping Degree
3.3. Experiment III: WBC Segmentation by Improved Watershed Algorithm
3.4. Experiment IV: Cell Counting by Proposed Method
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Hiremath, P.S.; Bannigidad, P.; Geeta, S. Automated identification and classification of white blood cells (Leukocytes) in digital microscopic image. IJCA Spec. Issue Recent Trends Image Processing Pattern Recognit. 2010, 2, 59–63. [Google Scholar]
- Taherisadr, M.; Nasirzonouzi, M.; Baradaran, B.; Mehdizade, A. New approach to red blood cell classification using morphological image processing. J. Shiraz E-Med. 2013, 14, 44–54. [Google Scholar]
- Mao, K.Z.; Zhao, P.; Koh, T.S.; Tan, P.H. Overlapping/touching cell nuclei segmentation based on analysis of perpendicular distance curve. In Proceedings of the IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, Kyoto-Osaka-Nara, Japan, 20–22 October 2003; pp. 212–213. [Google Scholar]
- Nguyen, N.T.; Duong, A.D.; Vu, H.Q. A new method for splitting clumped cells in red blood images. In Proceedings of the IEEE 2nd International Conference on Knowledge and Systems Engineering, Hanoi, Vietnam, 7–9 October 2010; pp. 3–8. [Google Scholar]
- Khan, H.A.; Maruf, G.M. Counting clustered cells using distance mapping. In Proceedings of the IEEE International Conference on Informatics, Electronics, and Vision (ICIEV), Dhaka, Bangladesh, 17–18 May 2013; pp. 1–6. [Google Scholar]
- Fan, J.P.; Zhang, Y.L.; Wang, R.C.; Li, S.G. A separating algorithm for overlapping cell image. J. Softw. Eng. Appl. 2013, 6, 179–183. [Google Scholar] [CrossRef] [Green Version]
- Lu, Z.; Carneiro, Z.; Bradley, A.P. An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Trans. Image Processing 2015, 24, 1261–1272. [Google Scholar]
- Phoulady, H.A.; Goldgof, D.B.; Hall, L.O.; Mouton, P.R. A new approach to detect and segment overlapping cells in multi-layer cervical cell volume images. In Proceedings of the IEEE 13th International Symposium on Biological Imaging, Prague, Czech Republic, 13–16 April 2016; pp. 201–204. [Google Scholar]
- Lim, H.N.; Mashor, Y.; Supardi, N.Z.; Hassan, R. Colour and morphological based techniques on white blood cells segmentation. In Proceedings of the IEEE 2nd International Conference on Biomedical Engineering, Penang, Malaysia, 30–31 March 2015. [Google Scholar]
- Liu, Z.; Liu, J.; Xian, X.Y.; Yuan, H.; Li, X.M.; Chang, J.; Zheng, C.Y. Segmentation of white blood cells through nucleus mark watershed operations and mean shift clustering. Natl. Cent. Biotechnol. Inf. 2015, 15, 22561–22586. [Google Scholar] [CrossRef] [PubMed]
- Sharif, J.M.; Miswan, M.F.; Ngadi, M.A.; Salam, M.S.H.; bin Abdul Jamil, M.M. Red blood cell segmentation using masking and watershed algorithm: A preliminary study. In Proceedings of the 2012 International Conference on Biomedical Engineering, Macau, China, 28–30 May 2012; pp. 258–262. [Google Scholar]
- Li, S.; Buehnemann, C.; Hassan, B.; Noble, J.A. Segmentation of cell clumps for quantitative analysis. In Proceedings of the Engineering in Medicine and Biology Society (EMBC) 2010 Annual International Conference of the IEEE, Washington, DC, USA, 13–17 December 2010; pp. 4813–4816. [Google Scholar]
- Roerdink, J.B.; Meijster, A. The watershed transform: Definitions, algorithms and parallelization strategies. Fundam. Inf. 2000, 41, 187–228. [Google Scholar] [CrossRef] [Green Version]
- Bieniecki, W. Oversegmentation avoidance in watershed-based algorithms for color images. In Proceedings of the Modern Problems of Radio Engineering, Telecommunications and Computer Science International Conference, Washington, DC, USA, 18–20 November 2004; pp. 169–172. [Google Scholar]
- Bala, A. An Improved watershed image segmentation technique using MATLAB. Int. J. Sci. Eng. Res. 2012, 3, 1–4. [Google Scholar]
- Amoda, N.; Kulkarni, R.K. Image segmentation and detection using watershed transform and region-based image retrieval. Int. J. Emerg. Trends Technol. Comput. Sci. 2013, 2, 89–94. [Google Scholar]
- Arslan, S.; Ozyurek, E.; Gunduz-Demir, C. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytom. Part A 2014, 85, 480–490. [Google Scholar] [CrossRef] [PubMed]
- Zack, G.W.; Rogers, W.E.; Latt, S.A. Automatic measurement of sister chromatid exchange frequency. J. Histochem. Cytochem. 1977, 25, 741–753. [Google Scholar] [CrossRef] [PubMed]
- Putzu, L.; Ruberto, C. White Blood Cells Identification and Counting from Microscopic Blood Image. Int. J. Med Health Sci. 2013, 7, 20–27. [Google Scholar]
- Fatichah, C.; Purwitasari, D.; Hariadi, V.; Effendy, F. Overlapping white blood cell segmentation and counting on microscopic blood cell images. Int. J. Smart Sens. Intell. Syst. 2014, 7. [Google Scholar] [CrossRef] [Green Version]
- Reta, C.; Robles, L.A.; Gonzalez, J.A.; Diaz, R.; Guichard, J.S. Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute leukaemia. In Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, Melbourne, FL, USA, 19–21 May 2010; pp. 86–91. [Google Scholar]
- Sadeghian, F.; Seman, Z.; Ramli, A.R.; Kahar BH, A.; Saripan, M.I. A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol. Proced. Online 2009, 11, 196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Image | X (Manual) | Y (Auto) | X | X2 | Y2 |
---|---|---|---|---|---|
1 | 9.3 | 7.5 | 69.75 | 86.49 | 56.25 |
2 | 25.2 | 23.5 | 592.2 | 635.04 | 552.25 |
3 | 32.5 | 31 | 1007.5 | 1056.25 | 961 |
4 | 42.4 | 41.5 | 1759.6 | 1797.76 | 1722.25 |
5 | 61 | 57.5 | 3507.5 | 3721 | 3306.25 |
6 | 10.8 | 6.3 | 68.04 | 116.64 | 39.69 |
7 | 21.6 | 17.2 | 371.52 | 466.56 | 295.84 |
8 | 35.6 | 35 | 1246 | 1267.36 | 1225 |
9 | 52.1 | 42.2 | 2198.62 | 2714.41 | 1780.84 |
10 | 53.6 | 47.7 | 2556.72 | 2872.96 | 2275.29 |
Total | 344.1 | 309.4 | 13,377.45 | 14,734.47 | 12,214.66 |
WBC Segmentation | |||
---|---|---|---|
Total Number of Cells | Success Segmented | ||
Number | Percentage | ||
Watershed Transform Method | 206 | 51 | 24.76 |
Proposed Method | 206 | 127 | 61.65 |
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Kiu, S.M.; Wang, Y.C. Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia. BioMedInformatics 2022, 2, 234-243. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedinformatics2020015
Kiu SM, Wang YC. Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia. BioMedInformatics. 2022; 2(2):234-243. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedinformatics2020015
Chicago/Turabian StyleKiu, Siew Ming, and Yin Chai Wang. 2022. "Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia" BioMedInformatics 2, no. 2: 234-243. https://0-doi-org.brum.beds.ac.uk/10.3390/biomedinformatics2020015