A New Type of Using Morphology Methods to Detect Blood Cancer Cells
In order to resolve the problem of recognizing blood cancer cells accurately and effectively, an identifying and classifying algorithm was proposed using grey level and color space. After image processing, blood cells images were gained by using denoising, smoothness, image erosion and so on. After that, we use granularity analysis method and morphology to recognize the blood cells. And then, calculate four characterizes of each cell, which is, area, roundness, rectangle factor and elongation, to analysis the cells. Moreover, we also applied the chromatic features to recognize the blood cancer cells. The algorithm was testified in many clinical collected cases of blood cells images. The results proved that the algorithm was valid and efficient in recognizing blood cancer cells and had relatively high accurate rates on identification and classification.
Keywordsimage processing mathematical morphology image denoising granularity detection cell clustering cell recognition
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- 2.Sinha, N., Ramakrishnan, A.G.: Automation of Differential Blood Count. Digital Object Identifier 2(15-17), 547–551 (2003)Google Scholar
- 3.Yin, C., Luan, Q., Feng, N.: Microscopic Image Analysis and Recognition on Pathological Cells. Journal of Biomedical Engineering Research 28(1), 35–38 (2009)Google Scholar
- 4.Rafael, C.G., Richard, E.W.: Digital Image Processing, 2nd edn. Prentice Hall (2002)Google Scholar
- 5.Debayle, J., Pinoli, J.C.: Multi-scale Image Filtering and Segmentation by Means of Adaptive Neighborhood Mathematical Morphology. In: Proc. of IEEE International Conference on Image Processing, Genova, Italy, vol. 3, pp. 537–540 (2005)Google Scholar
- 6.Tang, X., Lin, X., He, L.: Research on Automatic Recognition System for Leucocyte Image. Journal of Biomedical Engineering 24(6), 1250–1255 (2007)Google Scholar
- 7.Funt, B., Barnard, K., Martin, L.: Is machine colour constancy good enough? In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 445–459. Springer, Heidelberg (1998)Google Scholar
- 8.Huimin, L., Lifeng, Z., Seiichi, S.: A Method for Infrared Image Segment Based on Sharp Frequency Localized Contourlet Transform and Morphology. In: IEEE International Conference on Intelligent Control and Information Processing, Dalian, China, pp. 79–82 (2010)Google Scholar