Study of Colour Model for Segmenting Mycobacterium Tuberculosis in Sputum Images

• Published in: IOP conference series. Materials Science and Engineering 2018-03, Vol.325 (1), p.12010
• Main Authors: Kurniawardhani, A, Kurniawan, R, Muhimmah, I, Kusumadewi, S
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Color; Image contrast; Image segmentation; Tuberculosis
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/325/1/012010

One of method to diagnose Tuberculosis (TB) disease is sputum test. The presence and number of Mycobacterium tuberculosis (MTB) in sputum are identified. The presence of MTB can be seen under light microscope. Before investigating through stained light microscope, the sputum samples are stained using Ziehl-Neelsen (ZN) stain technique. Because there is no standard procedure in staining, the appearance of sputum samples may vary either in background colour or contrast level. It increases the difficulty in segmentation stage of automatic MTB identification. Thus, this study investigated the colour models to look for colour channels of colour model that can segment MTB well in different stained conditions. The colour models will be investigated are each channel in RGB, HSV, CIELAB, YCbCr, and C-Y colour model and the clustering algorithm used is k-Means. The sputum image dataset used in this study is obtained from community health clinic in a district in Indonesia. The size of each image was set to 1600x1200 pixels which is having variation in number of MTB, background colour, and contrast level. The experiment result indicates that in all image conditions, blue, hue, Cr, and Ry colour channel can be used to segment MTB in one cluster well.