Proceedings Volume 2010 (format .pdf) - SimpBTH

staining was performed as previously shown (Ardelean et al., 2009 b; Ghita &Ardelean, 2010).Epifluorescence microscopy. Samples were viewed immediately byepifluorescence microscopy (N-400FL, lamp Hg 100 W, type on the blue filter-450-480 nm) with immersion 100X objective and 10X eyepieces. Cell enumerationwas done manually (Fry, 1990; Sherr et al., 2001), 400-600 cells being counted oneach filter, and this number of cells was converted to cells/mL following classicequation (Fry, 1990; Sherr et al., 2001), using a calibrated square eye piece(surface 0.01mm 2 ), as previously shown (Ardelean et al.,2009a; Ghita & Ardelean,2010).The automatic cell analysis was performed using the CellC software, the mainsoftware used for multiple digital microscope images analysis to count cells(http://www.cs.tut.fi/sgn/csb/cellc/). ImageJ software was applied to digital imagesof whole cells color-stained bacteria, used to display, edit, analyze, process, saveand print 8–bit, 16–bit and 32–bit epifluorescence digital images, many imageformats including TIFF, JPEG, BMP, supporting ‘stacks’and hyperstacks, a seriesof images that share a single window; also we used this software for measure thelength of cells and pixel value statistics of user-defined selections, creating densityhistograms and line profile plots, supports standard image processing functionssuch as contrast manipulation, sharpening, smoothing, edge detection and medianfiltering. Because the digital images are two-dimensional grids of pixel intensitiesvalues with the width and height of the image being defined by the number ofpixels in x (rows) and y (columns) direction, that’s way the pixels are the smallestsingle components of images, holding numeric values – pixel intensities – thatrange between black and white (ImageJ user guide). Microphotographs used in thisstudy was RGB images, RGB/HSB stacks, and composite images.CellC software proceeds few important steps: the background is separated fromthe objects based on the intra-class variance threshold method; noise and specks ofstaining color in the image can affect the reliability of the analysis, so those wasremoved. The removal was done applying mathematical morphology operations tothe image; then separation of clustered objects was performed (Selinummi, 2008).The CellC software was used for cell enumeration and measurements of cell’sproperties (size, shape, intensity). We applied the algorithms of CellC software fordigital images, because this have three important parts: a MATLAB figure file ofthe segmented image (this can be exported in any common image file format; acomma separated value (CSV) - file with quantitative data of the cells (was openedin a spreadsheet program Excel for further analysis); a summary CSV-file with thecell count for each of the analyzed images for a quick overview of the analysisprocess (this file were only saved in the batch processing mode). Fluorescencemicroscopy digital images were analyzed and the objects has different intensitythan the background.206