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CMEIAS 1-D Object Classification

The CMEIAS/IT 1-Dimensional Object Classifier sorts objects in an image based on division of a scale defined by a single measurement feature, in units defined by user-specified spatial calibration. Any one of 7 size, 8 shape or 7 luminosity measurement attributes can be used. The user enters the upper class limit value for each bin class (up to 16 total) to define the range for the specified measurement attribute, one-at-a-time. Alternatively, a previously saved *.ocd calibration file can be loaded to automatically enter the upper class limit values within the input fields. The full automatic output from this object classifier includes a table listing each bin value range, corresponding object counts for each bin class, the mean and std. dev. of measurement values for all objects in each bin, and a copy of the analyzed image with each cell pseudocolored according to its assigned classification. Shown below are examples of image analysis using this 1-D object classifier performed on segmented images of bacteria classified into bins according to their optimized size (area) and shape (width:length ratio) attributes, respectively. The CMEIAS 1.28 interactive training tutorial includes a section of image analysis using this 1-D object classifier. Also, 20 calibration files are provided to help the user optimize the range of upper class limits for image analysis of objects using this object classifier. The CMEIAS 1.28 interactive training tutorial includes a section of image analysis using this object classification routine.

Size distribution analysis of bacteria using the CMEIAS/IT 1-D object classifier with optimized area bins. Shown are the original image, the dialog box to specify the measurement attribute (area) and optimized upper class limits, the pseudocolored classification image, and the output of classification data in the Results window.
size distribution analysis screenshots


Cell shape classification using a single measurement feature and the CMEIAS/IT 1-D classifier. The example shown is a morphotype classification of cocci (blue), regular rods (green), and unbranched filaments (red) using the Width:Length Ratio measurement feature and optimized upper class limits of 0.0625 and 0.5.

define classes and results screenshots