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MYCOBACTERIUM TUBERCULOSIS BACILLI CELLS IDENTIFICATION USING MOMENT INVARIANT

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Author(s): JADHAV M.E., KALE K.V. and BAHETI M.J.

Journal: International Journal of Machine Intelligence
ISSN 0975-2927

Volume: 3;
Issue: 3;
Start page: 146;
Date: 2011;
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Keywords: Mycobacterium Tuberculosis (M.TB) | ZN-Stained (Ziehl –Neelsen) | Moment invariant | Gaussian membership function.

ABSTRACT
Mycobacterium Tuberculosis(M.TB) bacilli is causative agent of Tuberculosis. Manual detection method for M.TBis time consuming, tedious & sometime it may confuse with some stain residue & non tuberculosis bacilli. For this reason theneed of atomization is required for exact identification of tuberculosis. We present a new method for M.TB bacilli cellsrecognition method using Moment invariant and Gaussian member function. The object i.e. M.TB bacilli cells are extractedusing color segmentation method, from Ziehl-Neelsen (ZN) stained sputum smears images. These images have blue colorbackground on which red color M.TB bacilli cells. The objects are extracted using color segmentation (thresholding) methodand boundary detection. The various extracted shapes contain M. TB bacilli cells, non TB cells, stain residue etc, theseextracted objects are preprocess using morphological dilation and then moment invariant feature extracted. Our mainobjective is to recognizes M.TB bacilli cells with the help of color segmentation & shape base feature moment invariantsusing Gaussian member function. The recognition rate of M.TB bacilli cells is obtaining 98.17%.
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