Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection

TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken. The proposed algorithms were implemented using MATLAB version 9.2. The comparative analysis of results has been done using PASW statistics version 18. The comparison between results from proposed algorithm and reference standard data results was done. The proposed bacilli segmentation algorithm gave an accuracy of 94%, whereas the proposed chest radiography algorithm gave an accuracy of 92%. The accuracy of the two algorithms was found to be good being above 90, so we can use any of these algorithms for screening of TB patients. These will make the screening process robust and more reliable. Moreover, the proposed systems are expected to reduce laborious fatigue and human errors. The proposed algorithms will assist physicians, doctors and microbiologists in screening of TB patients. Further work could be done to detect other abnormalities of lungs i.e. lung cancer and heart diseases using the proposed chest radiography algorithm. Bacilli segmentation is done on the basis of color, so for future work one could also consider size and shape parameters of bacilli to make the system better.