Tuberculosis Bacteria Segmentation in Acid Fast Stained Images

This paper presents a novel method for tuberculosis bacteria segmentation in microscopic images. Manual identification of the bacterial cell is a very difficult process. The automation in tuberculosis bacteria detection is the object of this article using microscopic image processing. In the proposed segmentation method, firstly image enhancement is done followed by the bacteria region masking. Further, the marking of bacteria points is performed by the marked point process model. Finally, the complete bacteria are identified by the superellipse and supervised variational contour models. MATLAB simulation results confirm the superiority of the proposed method as compared with the state of the art methods, on the basis of segmentation accuracy, F1-score, and Dice similarity coefficient.