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

International Conference on Physical Instrumentation and Advanced Materials

物理学;材料科学

Rulaningtyas, Riries^1 ; Suksmono, Andriyan B.^2 ; Mengko, Tati L. R.^2 ; Saptawati, Putri^2

Biomedical Engineering, Department of Physics, Faculty of Science and Technology, University of Airlangga, Kampus C, Jl. Mulyorejo, Surabaya

60115, Indonesia^1

School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha, Bandung

40132, Indonesia^2

关键词: Colour clustering;    Colour segmentation;    K;    means clustering;    Organizing map;    Sputum images;    Sputum smear images;    Tuberculosis bacillus;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/853/1/012012/pdf
DOI  :  10.1088/1742-6596/853/1/012012

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.