Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature

Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature

• By Merlina, Nita; Noersasongko, Edi; Nurtantio, pulung; Soeleman, M.A; Riana, Dwiza; Hadianti, Sri;
• Seminar Internasional Bereputasi
• 295
• 231 - 239

Color image segmentation on cytoplasm Pap smear single cell image identified as normal condition is an interesting subject to study. It is caused by the image limitation and morphological transformation complexity of the cell structural part. Feature analysis on cytoplasm area is an important thing in the process of biomedical image analysis because of the noise and complex background and the bad cytoplasm contrast as well. Thus, an analysis on the feature area on cytoplasm automatically is an urge thing to do to identify Pap smear normal cell image based on feature analysis on cytoplasm area in single cell image identified as normal condition. The purpose of this research is to analyze how far the process color image segmentation on cytoplasm by using normal single cell image is able to produce features of texture and form analysis. To analyze the form of cytoplasm, this research used RGB color to HSV color conversion method which produces metric and eccentricity value. It is then continued to the process of threshold image and counting the wide area by changing threshold into binary image. On the other side, to analyze the texture, this research applied an analysis using gray-level co-occurrence matrix (GLCM) using K-means method to produce contrast, correlation, energy, and homogeneity parameters. The result of the research is the segmentation outcome to Pap smear normal single cell image sample to get metric, eccentricity, contrast, correlation, and energy features.

• Katakunci Image Pap smear Segmentation Color image Cytoplasm GLCM Cervical cancer
• URL https://link.springer.com/chapter/10.1007/978-981-15-5224-3_22
• Penerbit ,

REFERENSI

1. 1.
   Irawan, F.: Statistik penderita kanker di Indonesia (2018)Google Scholar
2. 2.
   Riana, D., Widyantoro, D.H., Mengko, T.L.: Extraction and classification texture of inflammatory cells and nuclei in normal pap smear images. In: 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (2015)Google Scholar
3. 3.
   Kusuma, F.: Tes Pap Dan Cara Deteksi Dini Kanker Serviks Lainnya, Departemen Obstetri dan Ginekologi Fakultas Kedokteran Universitas Indonesia RSUPN Dr. Cipto Mangunkusumo, Prevention and Early Detection of Cervical Cancer (PEACE), Jakarta (2012)Google Scholar
4. 4.
   Plissiti , S.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., Charchanti, A.: Dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In: 25th IEEE International Conference on Image Processing (2018)Google Scholar
5. 5.
   Lu, Z., Carneiro, G., Bradley, A.P.: Automated nucleus and cytoplasm segmentation of overlapping cervical cells. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol 8149. Lecture Notes in Computer Science, pp. 452–460 (2013)Google Scholar
6. 6.
   Zhao, L., Li, K. Wang, M., Yin, J., Zhu, E., Wu, C., Wang, S., Zhu, C.: Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF 46. Comput. Biol. Med. 71, 46 (2016). ISSN 00104825Google Scholar
7. 7.
   Riana, D., Hidayanto, A.N., Widyantoro, D.H., Mengko, T.L.R., Kalsoem, O.: Segmentation of overlapping cytoplasm and overlapped areas in Pap smear images. In: Information, Intelligence, Systems & Applications (IISA), pp. 1–5 (2017)Google Scholar
8. 8.
   Sun, G., Li, S., Cao, Y., Lang, F.: Cervical cancer diagnosis based on random forest. Int. J. Performability Eng. (2017)Google Scholar
9. 9.
   Riana, D., Ramdhani, Y., Prasetio, R.T., Hidayanto, A.N.: Improving hierarchical decision approach for single image classification of pap smear. Int. J. Electr. Comput. Eng. 8, 5415–5424 (2018)Google Scholar
10. 10.
    Riana, D., Tohir, H., Hidayanto, A.N.: Segmentation of Overlapping Areas on Pap Smear Images with Color Features Using K-Means and Otsu Methods (2018). https://ieeexplore.ieee.org/xpl/conhome/8767368/proceeding
11. 11.
    Song, Y., Qin, J., Lei, B., He, S., Choi, K.-S.: Joint shape matching for overlapping cytoplasm segmentation in cervical smear images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Venice, Italy, pp. 191–194 (2019)Google Scholar
12. 12.
    Nehra, S., Raheja, J.L., Butte, K., Zope, A.: Detection of cervical cancer using GLCM and support vector machines, pp. 49–53. https://ieeexplore.ieee.org/xpl/conhome/8768798/proceeding
13. 13.
    Pamungkas, A.: Pemrograman Matlab (2019). [Online]. Available: https://pemrogramanmatlab.com/pengolahan-citra-digital/segmentasi-citra
14. 14.
    Bhan, S., Vyas, A., Mishra, G.: Supervised segmentation of overlapping cervical pap smear images. In: 2016 International Conference on Signal Processing and Communications (2016)Google Scholar