Color Canals Modification with Canny Edge Detection and Morphological Reconstruction for Cell Nucleus Segmentation and Area Measurement in Normal Pap Smear Images

Color Canals Modification with Canny Edge Detection and Morphological Reconstruction for Cell Nucleus Segmentation and Area Measurement in Normal Pap Smear Images

• By Riana, Dwiza; Dewi, Dyah, Ekashanti, Octorina; Widyantoro, Dwi, H; Mengko, Tati, Latifah, R;
• Seminar Internasional Bereputasi
• 91
• 414-417

Abstract. This paper presents a cell nucleus segmentation and area measurement of Pap smear images by means of modification of color canals with Canny edge detection and morphological reconstruction methods. Cell nucleus characterization plays an important role for classifying the degree of abnormality in cervical cancer. The aim of this work is to find the matched measurement method with the manual nucleus area measurement. In this work, we utilized pap smear single cell images from Herlev data bank in RGB mode. The cell images were selected from 90 normal class

subjects that include: Normal Superficial, Normal Intermediate, and Normal Columnar classes. The nucleus of each cell

image was cropped manually to localize from the cytoplasm. The color canals modification was performed on each

cropped nucleus image by, first, separating each R, G, B, and grayscale canals, then implementing addition operation

based on color canals (R+G+B, R+G, R+B, G+B, and grayscale). The Canny edge detection was applied on those

modifications resulting in binary edge images. The nucleus segmentation was implemented on the edge images by

performing region filling based on morphological reconstruction. The area property was calculated based on the

segmented nucleus area. The nucleus area from the proposed method was verified to the existing manual measurement

(ground truth) of the Herlev data bank. Based on thorough observation upon the selected color canals and Canny edge

detection. It can be concluded that Canny edge detection with R+G+B canal is the most significant for all Normal

classes (r 0,305, p-value 0.05). While for Normal Superficial and Normal Intermediate, Canny edge detection is

significant for all RGB modifications with (r 0.414 – 0.817 range, , p-value 0.05), and for Normal Columnar, Canny

edge detection is significant for R+B canal (r 0.505, p-value 0.05).

• Katakunci Keywords: Pap smear images, nucleus, color canals, Canny detection, morphological reconstruction, cervical cancer PACS: 87.57.N
• URL https://aip.scitation.org/doi/10.1063/1.4868832
• Penerbit AIP Author Sercices, China China

REFERENSI

1. J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, “Pap-smear Benchmark Data For Pattern Classification”, Technical University of Denmark, Denmark, 2005.

2. Martin, Erik. Pap-Smear Classification. Technical University of Denmark – DTU.2003. http://fuzzy.iau.dtu.dk/download/martin2003.

3. Kale, As and Aksoy, Selim,” Segmentation of Cervical Cell Images”, International Conference on Pattern Recognition,IEEE,2010.

4. Riana, Dwiza, Dewi, Deo, Widyantoro, Dwi H and Tati,LM, Segmentasi Luas Nukeus Sel Normal Superfisial Pap smeae Menggunakan Operasi Kanal Warna dan Deteksi Tepi”. Seminar Nasional Inovasi Teknologi. 2012.

5. J. Canny, “A computational approch to edge detection,” IEEE Trans.Pattern Anal. Machine Intell., vol. PAMI-8, pp. 679–714, 1986.

6. J. Canny, “Finding edges and lines in images,” MIT Artif. Intell. Lab.,Cambridge, MA, Tech. Rep. AI-TR- 720, 1983.

7. Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp. 173-174.

8. 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.

9. P. Bamford and B. Lovell, “Unsupervised cell nucleus segmentation with active contours,” Signal Process., vol. 71, no. 2, pp. 203–213, 1998.

10. 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.

11. 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

12. E. Bak, K. Najarian, and J. P. Brockway, “Efficient segmentation framework of cell images in noise environments,” in Proc. 26th Int. Conf. IEEE Eng. Med. Biol., Sep., 2004, vol. 1, pp. 1802–1805.

13. N. A. Mat Isa, “Automated edge detection technique for Pap smear images usingmoving K-means clustering and modified seed based region growing algorithm,” Int. J. Comput. Internet Manag., vol. 13, no. 3, pp. 45–59,2005.

14. 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.