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

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 color, because of no standardization in staining. The sputum has more variants color and they are difficult to be identified. For helping the clinicians, this research examined the Self Organizing Map method for coloring image segmentation in sputum images based on color 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 colors adaptively.