Spatial Fuzzy C-means dan Rapid Region Merging untuk Pemisahan Sel Kanker Payudara
Abstract
Segmentation and overlapped cells separation are important phases in microscopic image processing of breast cancer, because the accuracy of overlapped cells separation result determines the accuracy of breast cancer cell calculation. The amount of breast cancer cells is considered by doctor in determining the action towards patients. Two of the most common topics discussed in previous studies are the problem of increasing the accuracy of overlapped cancer cell separation result by calculating the number of cancer cell and over-segmentation problem. Compared to watershed method, clustering method produces higher accuracy in separating overlapped cancer cells. In this paper, a combination of Spatial Fuzzy C-Means (SFCM) and Rapid Region Merging (RRM) method is proposed to separate the overlapped cells and handling the over-segmentation problem. The input image of overlapped cells separation phase is the result of breast cancer cell identification by Gram-Schmidt (GS) method, while the clustered cancer cells are overlapped cancer cells which are detected based on the area of geometric feature. 40 microscopic breast cancer cells image of benign and malignant type is used as the datasets. The average value of Mean Square Error (MSE) for cell identification is 0.07 and the average accuracy of overlapped cells separation using SFCM and RRM is 78.41%.
References
C. Fatichah and N. Suciati, “Nuclei Segmentation of Microscopic Breast Cancer Image using Gram-Schmidt and Cluster Validation Algorithm,” ICCSCE , pp. 27–29, November, 2015.
A. Mouelhi, M. Sayadi, and F. Fnaiech, “A Supervised Segmentation Scheme Based on Multilayer Neural Network and Color Active Contour Model for Breast Cancer Nuclei Detection,” ICEESA, 2013.
S. Xie, L. Chen, J. Chen, and H. Nie, “Image segmentation using iterative watershed and ridge detection,” J. Comput. Appl., vol. 29, no. 10, pp. 2668–2670, 2009.
A. Mouelhi, M. Sayadi, and F. Fnaiech, “Automatic segmentation of clustered breast cancer cells using watershed and concave vertex graph,” 2011 Int. Conf. Commun. Comput. Control Appl. CCCA 2011, no. 1, pp. 2–7, 2011.
A. Mouelhi, M. Sayadi, F. Fnaiech, and S. Member, “Hybrid Segmentation of Breast Cancer Cell Images Using a New Fuzzy Active Contour Model and an Enhanced Watershed Method”, CoDIT, no. 1, pp. 382–387, 2013.
N. Aini, C. Fatichah, and B. Amaliah, “Pemisahan Sel Bertumpuk Citra Sel Kanker Payudara Menggunakan Metode Region-Based Active Contour dan Bayesia,” SCAN-Jurnal Teknologi Informasi dan Komunikasi, pp. 1–7, 2015.
C. Fatichah, D. Purwitasari, V. Hariadi, and F. Effendy, “Overlapping White Blood Cell Segmentation And Counting on Microscopis Blood Cell Image,” International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 3, pp. 1271–1286, 2014.
P. Phukpattaranont and P. Boonyaphiphat, “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images,” Communications, vol. 5, no. 2, pp. 158–164, 2007.
Y. Li and Y. Shen, “Fuzzy c-means clustering based on spatial neighborhood information for image segmentation,” Journal of Systems Engineering and Electronics, vol. 21, no. 2, pp. 323–328, 2010.
S. Z. Beevi, M. M. Sathik, K. Senthamaraikannan, and J. H. J. Yasmin, “A RobustT Fuzzy Clustering Technique With Spatial Neighborhood Information for Effective Noisy Medical Image Segmentation,” ICCCNT, 2010.
Y. Chen and J. Chen, “A Watershed Segmentation Algorithm Based on Ridge Detection and Rapid Region Merging”, ICSPCC, 2014.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
