1. A method for automated analysis and diagnosis of the presence of a bacterium in a histological specimen, comprising: providing a histological specimen-stained slide; obtaining a color scanned image of the slide with a digital camera using visible light illumination; associating identification data with the slide; transforming the color scanned image into a single band image; searching parts of the single band image that match a template pattern identified by gray level values, wherein parts matching the pattern are candidate object components, said searching including segmenting the image and identifying closed object areas in the image using edge detection and completion; for each closed object, calculating spatial two dimensional second moments; obtaining an image covariance matrix; calculating eigenvectors and eigenvalues of the image covariance matrix; and for each closed object, testing if a total area and the covariance eigenvalues, correspond to those of images of Helicobacter pylori ; and declaring the closed image a candidate object component or not a candidate object component based on the results of said testing; classifying the candidate object components with a subsequent additional machine learning classifier; creating from said machine learning classifier an automatic decision support diagnosis report of the presence or absence of the bacterium in the specimen; and storing images with associated metadata and the decision support diagnosis report in a computer readable medium.