Segmentation of TB Bacilli in Ziehl-Neelsen Sputum Slide Images using k-means Clustering Technique

Rafikha Raof, M. Y. Mashor, S. S. M. Noor


Image segmentation is the most crucial steps in determining the accuracy of a medical diagnosis system that is based on image processing procedures. Therefore, it is important to select a suitable image segmentation technique to obtain good results and hence providing optimum accuracy for the developed diagnostic system. In this research, image segmentation procedure using k-means clustering approach has been considered for differentiating between pixels that represent TB bacilli and pixels that represents sputum or background. This paper presents the technique used to separate the TB bacilli and its background from the Ziehl-Neelsen sputum slide images. The k-means clustering has been applied to those images followed by several extra rules. The resulted images show encouraging results, which indicate that the proposed segmentation method is able to filter out the TB bacilli pixels from the background pixels.

Kata Kunci

k-means clustering; TB Diagnosis; Image Segmentation

Teks Lengkap:



WHO (2011). WHO Report 2011: Global Tuberculosis Control

WHO (2007). International statistical classification of diseases and related health problems, 10th revision (ICD-10), 2nd Edition.

Salleh, Z., Mashor, M. Y., Mat Noor, N. R., Aniza, S., Abdul Rahim, N., Wahab, et al. (2007). Colour contrast enhancement based on bright and dark stretching for Ziehl-Neelsen slide images. Proc. Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2007), 205-208.

Osman, M. K., Mashor, M. Y., Saad, Z., & Jaafar, H. (2009). Contrast enhancement for Ziehl-Neelsen tissue slide images using linear stretching and histogram equalization technique. Proc. IEEE Symposium on Industrial Electronics & Applications, ISIEA 2009, 431-435

Veropoulos, K., Campbell, C., & Learmonth, G. (1998). Image processing and neural computing used in the diagnosis of tuberculosis. IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications (Digest No. 1998/514), 8/1-8/4.

Forero, M. G., Sroubek, F., & Cristobal, G. (2004). Identification of tuberculosis bacteria based on shape and color. Real-Time Imaging, 10, 251-262.

Costa, M., Filho, F. C., Sena, J., Salem, J., & de Lima, M. (2008). Automatic identification of mycobacterium tuberculosis with conventional light microscopy. Proc. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008, 382-385

Wang, W., Qin, Z., Rong, S., Rong, X., & Song, Y. (2008). A kind of method for selection of optimum threshold for segmentation of digital color plane image. 9th International Conference on Computer-Aided Industrial Design and Conceptual Design, 959 – 961.

Forero, M. G., Cristobal, G., & Borrego, J. A. (2003). Automatic identification techniques of tuberculosis bacteria. SPIE Proceedings of the Applications of Digital Image Processing XXVI, 5203, 71-81.

Veropoulos, K., Learmonth, G., Campbell, C., Knight, B., & Simpson, J. (1999). Automated identification of tubercle bacilli in sputum: A preliminary investigation. Analytical and Quantitative Cytology and Histology, 21(4), 277–281

Wilkinson, M. (1996). Rapid automatic segmentation of fluorescent and phase-contrast images of bacteria. Fluorescence Microscopy And Fluorescent Probes. New York, NY: Plenum Press.



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