An Adaptive Filtering Technique for Segmentation of Tuberculosis in Microscopic Images

Tuberculosis disease is one of the most leading cause of fatality worldwide. however, it can be reduced if diagnosed and treated on time. Normally the method name Ziehl-Neelsen is used to diagnose Tuberculosis and a human specialist analyzes it using an optical microscope to find tuberculosis bacilli. Since this process is time-consuming, an automatic bacilli recognition system allows the diagnosis process faster. In this work, an automatic tuberculosis bacilli segmentation system is developed. Initially, the input image is preprocessed by applying adaptive mean filter (AMD) to remove impulse noise and power law transformation to enhance the image then transform the color space from RGB to HSV. The HSV color space is more suitable for image processing because each element is isolated in it. Next, we employed the multi-level thresholding algorithm to correctly segment each bacillus in the input sample and improved 2.13% accuracy when compared to state-of-the-art techniques.