Identification of mycobacterium tuberculosis based on BP neural network

Effective identification of mycobacterium tuberculosis is the key to automatic analysis of mycobacterium tuberculosis in microscopic image.The algorithm of the extraction and identification of mycobacterium tuberculosis in the microscopic images of the Ziehl-Nelson stained bacteria were investigated in this study.Firstly,the microscopic images were enhanced using the morphology top-hat transform and segmented based on a threshold,and then the features,including color,shape and border irregularity were defined and calculated.Finally the mycobacterium tuberculosis was identified by BP neural network.The experiment results show that mycobacterium tuberculosis can be effectively detected and identified using the algorithm proposed in this paper.The accuracy has reached 98% using sever fold cross-validation on 1200 selected targets.