In general, cervical cancer is a main reason of women’ death in the world. More than 80% of women was died because of cervical cancer in the developing country. Pap Smear is one of a detection method towards the prevention of cervical cancer as the effort of that measurement disease. There are seven characteristics of Pap Smear single cell; one of them is Mild Light Dysplasia (MLD) which is categorized into abnormal class. This research is conducted to know the width area of nucleus segmentation on MLD class, by using operator of Canny detection with Color grayscale Canal operation. The obtained result shows that Canny method with Color grayscale Canal operation is not too effective to detect nucleus on MLD class, so that it can be expected to be consired to do Canal modification of RGB color. 

Kata Kunci: Cervical cancer, Mild Light Dysplasia, Segmentation, Canny detection, nucleus area


[1] Riana, Dwiza. Dyah, DEO. Widyantoro, Dwi H, and Mengko, Tati LR, “Segmentasi Luas Nukleus sel Normal Superfisial Pap Smear Menggunakan Operasi Kanal Warna dan Deteksi Tepi,” in Proc. Seminar Nasional Inovasi dan Teknologi –UBSI, Bandung, 2012, pp. A 275-280. [2] J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, Pap-smear Benchmark Data For Pattern Classification, Technical University of Denmark, Denmark, 2005. [3] P. Bamford and B. Lovell, “A water immersion algorithm for cytological image segmentation,” in Proc. APRS Image Segmentation Workshop, Sydney, Australia, 1996, pp. 75-79. [4] P. Bamford dan B. Lovell. “A water immersion algorithm for cytological image segmentation. Processing 71(2), pp. 203-213, 1998.1C. Xu and J. L. Prince, “Snakes, shapes and gradient vector flow,” IEEE Transaction on Image Processing, vol. 7, pp. 359-369, 1998. [5] H. S. Wu, J. Barba, and J. Gil, “A parametric fitting algorithm for segmentation of cell images,” IEEE Trans. Biomed. Eng., vol. 45, no. 3, pp. 400-407, Mar. 1998. [6] A. Garrido and N. P. de la Blanca, “Applying deformable templates for cell image segmentation,” Pattern Recognit., vol. 33, no. 5, pp. 821-832, 2000. [7] N. Lassouaoui and L. Hamami, “Genetic algorithms and multifractal segmentation of cervical cell images,” in Proc. 7th Int. Symp. Signal Process. Appl., 2003, vol. 2, pp. 1-4. [8] E. Bak, K. Najarian, and J. P. Brockway, “Efficient segmentation framework of cell images in noise environment,” in Proc. 26th Int. Conf. IEEE Eng. Med. Biol., Sep., 2004, vol. 1, pp. 1802-1805. [9] N. A. Mat Isa, “Automated edge detection technique for Pap Smear images using moving K-means clustering and modified seed based region growing algorithm,” Int. J. Comput. Internet Manag., vol. 13, no. 3, pp. 45-59, 2005. [10] C. H. Lin, Y. K. Chan, and C. C. Chen, “Detection and segmentation of cervical cell cytoplast and nucleus,” Int. J. Imaging Syst. Technol., vol. 19, no. 3, pp. 260-270, 2009. [11] Rulaningtyas, R, and Ain K, Edge Detection For Brain Tumor Pattern Recognition, in Proc Conf ICIC-BME, 2009. [12] Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp. 173-174.