Mycobacterium Tuberculosis Bacilli cells Recognition using Multiple Classifier
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 11
Abstract
Many computer science researchers have been working on developing and designing an automated system for fast & accurate recognition of tuberculosis (TB), which would ensure speed up of treatment process. Manually for proper identification of Mycobacterium tuberculosis require skilled person it is critical, time consuming and hectic too. Identification of TB bacilli manually sometime may confuse with some stain residue & non tuberculosis bacilli. Due to this we get the faulty results. For this reason need of atomization is required for exact identification of tuberculosis. We present an object, pattern (Mycobacterium Tuberculosis cell) recognition method using Support Vector Machine (SVM), k-nearest neighbor (Knn) and Gaussian membership function. The object i.e. M.TB bacilli cells are extracted using color segmentation method, from ZiehlNelson stained sputum smears images. These images have blue background on which red color M.TB bacilli cells. Images are by self developed data set, obtained using a bright field microscope. The various shapes of objects are extracted using segmentation method. The extracted shapes is the mixture of M. TB bacilli cells, non TB cells and few are stain residue etc. For proper recognition(M.TB cells) the shape based feature of these patterns are extracted on the basis of these futures data set the multiple classifiers like SVM, Knn and Gaussian are applied for recognition of M.TB bacilli cells. The recognition rate obtains using SVM is highest than Knn and Gaussian.
Authors and Affiliations
Jadhav Mukti E. , Kale K. V.
Keywords
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