Colour segmentation of multi variants tuberculosis sputum images using self organizing map

Riries Rulaningtyas, Andriyan B. Suksmono, Tati L.R. Mengko, Putri Saptawati

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)


Lung tuberculosis detection is still identified from Ziehl-Neelsen sputum smear images in low and middle countries. The clinicians decide the grade of this disease by counting manually the amount of tuberculosis bacilli. It is very tedious for clinicians with a lot number of patient and without standardization for sputum staining. The tuberculosis sputum images have multi variant characterizations in colour, because of no standardization in staining. The sputum has more variants colour and they are difficult to be identified. For helping the clinicians, this research examined the Self Organizing Map method for colouring image segmentation in sputum images based on colour clustering. This method has better performance than k-means clustering which also tried in this research. The Self Organizing Map could segment the sputum images with y good result and cluster the colours adaptively.

Original languageEnglish
Article number012012
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 7 Jun 2017
EventInternational Conference on Physical Instrumentation and Advanced Materials, ICPIAM 2016 - Surabaya, Indonesia
Duration: 27 Oct 2016 → …


Dive into the research topics of 'Colour segmentation of multi variants tuberculosis sputum images using self organizing map'. Together they form a unique fingerprint.

Cite this