ABSTRACT
This paper presents a new method for segmentation of tuberculosis bacillus in conventional sputum smear microscopy. The method comprises three main steps. In the first step, a scalar selection are made for characteristics from the following color spaces: RGB, HSI, YCbCr and Lab. The features used for pixel classification in the segmentation step were the components and subtraction of components of these color spaces. In the second step, a feedforward neural network pixel classifier, using selected characteristics as inputs, is applied to segment pixels that belong to bacilli from the background. In third step geometric characteristics, especially the eccentricity, and a new proposed color characteristic, the color ratio, are used to noise filtering. The best sensitivity achieved in bacilli detection was 91.5%.
Subject(s)
Microscopy/methods , Mycobacterium tuberculosis/isolation & purification , Humans , Mycobacterium tuberculosis/classification , Reproducibility of Results , Sensitivity and Specificity , Sputum/microbiologyABSTRACT
Bioassay-guided fractionation of the bark extract of Annona foetida afforded a new antileishmanial pyrimidine-beta-carboline alkaloid, N-hydroxyannomontine (1), together with the previously reported annomontine (2), O-methylmoschatoline (3), and liriodenine (4). The structure of compound 1 was established on the basis of extensive 1D and 2D NMR and MS analyses. This is the third reported pyrimidine-beta-carboline-type alkaloid and is particularly important for Annona genus chemotaxonomy. In addition, all compounds exhibit in vitro antileishmanial activity against promastigote forms of Leishmania braziliensis. Compounds 2 and 4 showed better activity than compounds 1 and 3 against L. braziliensis. Compound 2 was not active against L. guyanensis.
