Tuberculosis bacteria analysis in acid fast stained images of sputum smear

Abstract

Tuberculosis (TB) basically originates due to bacteria, influences the developing nations and disrupts their economy severely. TB like diseases are with a high mortality rate worldwide, but early detection highly increases the chances of survival. This paper presents a novel method for TB bacteria segmentation and classification using microscopic images (MI). Manual identification of the bacterial cell is a very difficult process. The automation in TB bacteria detection is the objective of this article using MI processing. The proposed segmentation method first performs the image enhancement followed by 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. The features are extracted using bag of visual words and handcrafted work for the image classification. Simulation results confirm the superiority of the proposed method as compared with the state-of-the-art methods.