Decision Support System for Screening of Tuberculosis from Chest Radiographs using Hybrid Dominant Features

Abstract

Tuberculosis is an infectious disease and becomes a major threat all over the world but still diagnosis of tuberculosis is a challenging task. In literature, chest radiographs are consid- ered as most commonly used medical image in under developed countries for the diagnosis of TB. Di erent methods have been proposed, some systems are too slow and some are expen- sive especially not a ordable in under developed areas. Our paper presents a methodology in which segmentation technique is applied to get lung eld region and dominant features are extracted for detection of TB.In segmentation technique, lung model is calculated according to similarity measure and kernel graph cuts segmentation technique is used to partition the image, through the kernel mapping of image data, into di erent graph cut regions. Kernel function is used to transform image data to make piecewise constant model of graph cuts be- comes applicable. Evaluation of deviation of transformed data is done by piecewise constant model and smoothness cost. Minimization of energy function contains graph cut iterations of image partitioning and region parameters are evaluated by using kernel function. After segmentation, di erent combinations of features are extracted based on intensities, shape and texture of chest radiograph and given to classi er for the detection of TB. To reduce dimensionality and increase e ciency, dominant features are computed and given to classi- er for the detection of tuberculosis (TB). The performance of our methodology is evaluated on three publically available standard datasets by using parameters such as accuracy, speci- city and sensitivity. The improvement performance is demonstrated by comparing result with recently proposed and published methods. The results showed that proposed method have high accuracy as compared to previous methods. This research will assist radiologists in saving their time.