title: Automatic identification of mycobacterium tuberculosis with conventional light microscopy.
abstract: This article presents an automatic identification method of mycobacterium tuberculosis with conventional microscopy images based on Red and Green color channels using global adaptive threshold segmentation. Differing from fluorescence microscopy, in the conventional microscopy the bacilli are not easily distinguished from the background. The key to the bacilli segmentation method employed in this work is the use of Red minus Green (R-G) images from RGB color format. In this image, the bacilli appear as white regions on a dark background. Some artifacts are present in the (R-G) segmented image. To remove them we used morphological, color and size filters. The best sensitivity achieved was about 76.65%. The main contribution of this work was the proposal of the first automatic identification method of tuberculosis bacilli for conventional light microscopy.
authors.affiliation: Universidade Federal do Amazonas/Centro de Pesquisa e Desenvolvimento em Tecnologia Eletrônica e da Informação-UFAM/CETELI, Manaus, Amazonas, Brazil. mcosta@ufam.edu.br
authors.forename: Marly G F
authors.initials: MG
authors.lastname: Costa
authors.forename: Cícero F F
authors.initials: CF
authors.lastname: Costa Filho
authors.forename: Juliana F
authors.initials: JF
authors.lastname: Sena
authors.forename: Julia
authors.initials: J
authors.lastname: Salem
authors.forename: Mari O
authors.initials: MO
authors.lastname: de Lima
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D009169:Mycobacterium tuberculosis / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity