Automatic analysis of image of surface structure of cell wall-deficient EVC

Some computer applications for cell characterization in medicine and biology, such as analysis of surface structure of cell wall-deficient EVC (El Tor Vibrio of Cholera), operate with cell samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and be reliable in order to estimate texture quality from images. Therefore, we introduce wavelet theory and mathematical morphology to analyse the cellular surface micro-area image obtained by SEM (Scanning Electron Microscope). In order to describe the quality of surface structure of cell wall-deficient EVC, we propose a fully automatic computerized method. The image analysis process is carried out in two steps. In the first, we decompose the given image by dyadic wavelet transform and form an image approximation with higher resolution, by doing so, we perform edge detection of given images efficiently. In the second, we introduce many operations of mathematical morphology to obtain morphological quantitative parameters of surface structure of cell wall-deficient EVC. The obtained results prove that the method can eliminate noise, detect the edge and extract the feature parameters validly. In this work, we have built automatic analytic software named "EVC.CELL".