Tuberculosis Detection in XRAY Images using Matlab -Image Processing Project
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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.
We used feature matching for extraction by clusters we find classifies the disease but we get that much accuracy
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.
- High accuracy
- Low complexity
- Easy to recognize module
- Bio medical
- Medical Imaging
- Testability medical analysis
- MATLAB 2013b
It is conclude that for normal chest radiographs offers lower values in features such as variance, third moment and entropy, but higher mean. And in case of TB chest radiograph’s offers lower mean value and other feature values were higher. Usually in TB case both the lungs are affected, spread of the infection on both sides is asymmetrical. TB chest radiographs offers greater disparities between the left and right on every feature values.
 R.P. Tripathi, N. Tewari, N. Dwivedi, et al. “Fighting Tuberculosis: An Old Disease with New Challenges”. Med Res Rev, 2005, 25(1), 93-131.
 R. Colebunders, WE. Bastian. “A Review of Diagnosis and Treatment of Smear-negative Pulmonary Tuberculosis”. Int. J. Tuberc Lung Dis, 2000, 4, 97-107.
 R. Gonzalez and R. E. Woods. Digital Image Processing. Prentice Hall, New Jersey, ISBN 0201180758, 2 edition, 2002.
 “Implementing the WHO Stop TB Strategy”, A Handbook for National TB Control Programmes, 2008.
 “WHO Policy on Collaborative TB/HIV activities”, Guidelines for national programmes and other stakeholders, Update Version of 2004.
 Tony Lindeberg, KTH Royal Institute of Technology, Stockholm, Sweden, Scale Invariant Feature Transform.
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