Automated Method of Analysing Sputum Smear Tuberculosis Images Using Multifractal Approach

Abstract

In this chapter, an attempt has been made to automate the analysis of positive and negative Tuberculosis (TB) sputum smear images using multifractal approach. The smear images (N=100) recorded under standard image acquisition protocol are considered. The images are subjected to multifractal analysis and the corresponding spectrum parameters are extracted. Most significant parameters are selected based on the principal component analysis. Further, these parameters are subjected to classification using support vector machine classifier with different kernels. Results demonstrate that the multifractal analysis is capable of discriminating positive and negative TB images. The values of apex, broadness and aperture of the singularity spectrum are higher for TB positive than negative images and are statistically significant. The performance estimators obtained in the classification process show that the polynomial kernel performs better. It appears that this method of texture analysis could be useful for automated analysis of TB using digital sputum smear images.