TY - JOUR
T1 - Colour segmentation of multi variants tuberculosis sputum images using self organizing map
AU - Rulaningtyas, Riries
AU - Suksmono, Andriyan B.
AU - Mengko, Tati L.R.
AU - Saptawati, Putri
N1 - Funding Information:
This research was supported by the grant from Directorate General of higher Education Indonesia. We would like to thank to Indonesia Government who already supported and gave the fund for this research. Special thanks went to Bandung Health Department in West Java Province, Indonesia which already supported Ziehl-Neelsen sputum smear slide data.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028551979&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/853/1/012012
DO - 10.1088/1742-6596/853/1/012012
M3 - Conference article
AN - SCOPUS:85028551979
SN - 1742-6588
VL - 853
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012012
T2 - International Conference on Physical Instrumentation and Advanced Materials, ICPIAM 2016
Y2 - 27 October 2016
ER -
