Improved segmentation of meteorite micro-CT images using local histograms

In micro-CT images of meteorites individual components such as matrix, chondrules, Ca,Al-rich inclusions (CAIs), and opaque phases (metal and sulfide) are visually distinguishable. Automated classification of the components is desirable to deal with the large amount of data in a 3-D CT image. Classification by pixel intensity achieves a performance only 25% of the way from baseline to perfect. The poor performance is explained by an overlap in the range of intensities present in the different components. An improved method of semiautomated classification is presented, based on local histograms of the intensity. This achieves a performance 60% of the way from baseline to perfect.

► In micro-CT images of meterorites different components can be visually distinguished. ► Automated methods for distinguishing these components would be useful. ► Simple thresholding performs poorly. ► A method based on local histograms of intensity performs much better.