Detection of mycobacterium tuberculosis in Ziehl-Neelsen stained tissue images using Zernike moments and hybrid multilayered perceptron network

Conventional clinical diagnosis of tuberculosis disease such as manual screening by microbiologist are tedious, laborious and time consuming. Therefore, more research has been carried out to develop technologies that able to automate the detection process. This paper presents an automated approach to tuberculosis bacilli detection in tissue section. The proposed approach employs image processing technique and neural network for the segmentation and detection of tuberculosis bacilli. First, images of tuberculosis bacilli in tissue samples are captured using light microscope after stained with Ziehl-Neelsen staining method. Then colour image segmentation using moving k-mean clustering is used to extract tuberculosis bacilli from the tissue image. Two colour spaces, RGB and C-Y colour, were utilised in order to improve the quality of segmentation and robust against various staining condition. Next, geometrical features of Zernike moments are calculated. From these features, the best features that could detect tuberculosis bacilli with higher accuracy were selected using hybrid multilayered perceptron network. Experimental results demonstrate that the proposed method is efficient and accurate to detect the tubercle bacilli in tissue.