Tuberculosis Detection in XRAY Images using Matlab

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Description

Tuberculosis (TB) is rapidly spread disease in the world. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection and lung decease classification. In this paper we present method for detection of Tuberculosis in X-Ray image by using MATLAB which includes Pre Processing of Image, Segmentation and Feature extraction from that image

TB infections needs to be X rayed and screen for active TB to ensure a proper treatment of their infections. Taking Standard Chest X-rays (CXRs) is an inexpensive way to screen for the presence of TB. The purpose of screening system is to identify everything that is or could be related to a patient having TB infections. But mass screening of a large population is a time consuming and tedious work, which require considerable effort when done manually. For this reason, Computer-aided diagnostic systems (CAD) used to detect Tuberculosis infections in chest X rayed. These systems have the potential to lessen the TB detection error risk and also depend on the radiologists. In this paper, we describe how we differentiate between normal and abnormal CXRs with manifestations of TB, using image processing techniques in MATLAB.

EXISTING SYSTEM

We used feature matching for extraction by clusters we find classifies the disease but we get that much accuracy

PROPOSED SYSTEM

Image processing system consists of image pre-processing we reduce the noise in segmentation we use Discrete wavelet transform further we proceed to? feature selection and extraction by that we can find appropriate determination of image .The image needs to be reduced to certain determining characteristics and the analysis of these characteristics gives the relevant information After the feature extraction stage, the features have to be analyzed, classifiers like Bayesian classifiers and Support Vector Machines (SVM) are used to classify features and find the best values for them.

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