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Carcinoma cell identification via optical microscopy and shape feature analysis

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DOI: 10.4236/jbise.2013.611128    3,154 Downloads   4,549 Views   Citations

ABSTRACT

Optical microscopy is commonly used for cancer cell detection. Focusing on carcinoma cell identification via optical microscopy, a proof-of-concept study was performed at Laboratory of Design, Optimization and Modeling (LCOMS) to determine the grade of cancer cells. This paper focuses on three types of abnormal cells; namely, Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN), which is a precursor state for cancer, and Carcinoma (Ca), which corresponds to abnormal tissue proliferation cancer. These types of cells were used to assess the efficiency of using shape features to identify carcinoma cells. A comparative study based on performance indicator concludes that three features, Area, Xor-Convex, and Solidity, were found to be effective in identifying the Carcinoma grade of cancer cells.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Chaddad, A. , Tanougast, C. , Golato, A. and Dandache, A. (2013) Carcinoma cell identification via optical microscopy and shape feature analysis. Journal of Biomedical Science and Engineering, 6, 1029-1033. doi: 10.4236/jbise.2013.611128.

References

[1] Seshadri Sriprasad, S., Feneley, M.R. and Thomson, P.M. (2000) History of prostate cancer treatment. Surgical Oncology, 18, 185-191.
http://dx.doi.org/10.1016/j.suronc.2009.07.001
[2] Yassin, A.Y., et al. (2006) Haematopoietic cell clusters quantification using image analysis. Biomedical Signal Processing and Control, 1, 282-288.
[3] Lovisa, B., et al. (2007) Improvement of the contrast in cancer detection by autofluorescence bronchoscopy using a narrowband spectral violet excitation: A preliminary study. Biomedical Signal Processing and Control, 2, 234-238. http://dx.doi.org/10.1016/j.bspc.2007.07.004
[4] Castanon, C.A.B., et al. (2007) Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria. Pattern Recognition, 40, 1899-910.
http://dx.doi.org/10.1016/j.patcog.2006.12.006
[5] Wang, X., et al. (2008) Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. Journal of Biomedical Informatics, 41, 264-271. http://dx.doi.org/10.1016/j.jbi.2007.06.008
[6] Mayumi, D., et al. (2008) A texture approach to leukocyte recognition. Real-Time Imaging, 10, 205-216.
[7] Chaddad, A., et al. (2011) Improving of colon cancer cells detection based on Haralick’s features on segmented histopathological images. IEEE Conference on Computer Applications and Industrial Electronics, 87-90.
[8] Chaddad, A., et al. (2011) Classification of cancer cells based on morphological features from segmented multispectral bio-images. 4th International Conference on Biomedical Electronics and Biomedical Informatics, 92-97.
[9] Tosun, A.B., et al. (2009) Object oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition, 42, 1104-1112.
http://dx.doi.org/10.1016/j.patcog.2008.07.007
[10] Cataldo, S.D., et al. (2010) Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: A quantitative evaluation. Computerized Medical Imaging and Graphics, 34, 453-461.
http://dx.doi.org/10.1016/j.compmedimag.2009.12.008
[11] Hodges, J. (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, chapter Nonparametric Discrimination: Small sample performance. IEEE Computer Society Press, Los Alamitos, Reprint of original work from 1952.
[12] Hart, P. (1968) The condensed nearest neighbour rule. IEEE Transactions on Information Theory, 14, 515-516.
http://dx.doi.org/10.1109/TIT.1968.1054155
[13] Kass, M., et al. (1988) Snakes: Active contour models. International Journal of Computer Vision, 1, 321-331.
http://dx.doi.org/10.1007/BF00133570
[14] Caselles, V., et al. (1993) A geometric model for active contours in image processing. Numerische Mathematik, 66, 1-31. http://dx.doi.org/10.1007/BF01385685
[15] Malladi, R., et al. (1995) Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 158-175.
http://dx.doi.org/10.1109/34.368173
[16] Stone, M. (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36, 111-147.
[17] Mathworks, Matlab Software (2013) Mathworks.
[18] Chaddad, A., et al. (2013) Hardware implementation of active contour algorithm for fast cancer cells detection. IEEE, 29th Southern Biomedical Engineering Conference, 129-130.

  
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