In material testing process, the assessment of 3D geometry from 2D microstructure images of materials using stereological methods seems to gain more importance. An exclusive field by name Stereology (collection of stereological methods) has evolved to address the quantitative analysis of materials. The stereology by manual practice is tiresome, time consuming and often produce biased results due to manual physiological limits. There is a definite need for automation of stereological procedures that can make greater impact on quality of quantitative analytical results. In this paper, an automated method to derive quantitative description of 3D geometry based on data obtained by quantitative image analysis of 2D digital microstructure images is proposed. The proposed method makes use of stereological parameters and digital image processing techniques for estimation of many stereological parameters (proposed by American Standard for Materials - ASM). The results obtained by proposed method correlate with the results obtained by manual methods satisfactorily. Further, it saves considerable amount of effort, time and cost in material testing process. Since the basic frame-work of the proposed method considers many quantifiable parameters which are otherwise difficult in manual process, it has practical significance in material testing laboratories.

1. ASM International Handbook Committee. 2004. ASM Hand book, Vol. 9, Metallography and Microstructures. ASM International, USA.
2. ASTM International. 2002. Standard Guide for Preparation of Metallographic Specimens, E3,Annual Book of ASTM Standards, Vol 3. 01.
3. ASTM International. 1999. Test Methods for Determining average grain size using semi automatic and automatic Image Analysis, E 1382-97,Annual Book ASTM Standards, Sec. 3, pp 867-890.
4. Russ J. C. 1999. Practical stereology, 2nd Edn. , Plenum Press, New York.
5. Underwood E. E. 1970. Quantitative Stereology, Addison-Wesley, USA.
6. Wiebel. 1980. Stereological Methods, Vol. 1:Practical Methods for Biological Morphometry, Academic Press, New York.
7. Deholf R. T. & Rhines. 1968. Quantitative microscopy, Mc. GrawHill, Newyork Otsu N. 1979.
8. A Threshold level selection method from gray level histograms, IEEE Trans. Sys. Man. , Cybernetics, Vol. 9, pp. 62-66.
9. Exner H. E. 1996. Qualitative and Quantitative Surface Microscopy, Physical Metallography, Elsevier Science Publications, pp. 993-1032.
10. Ohser J and Mucklish. 2005. Statistical analysis of microstructure in material science, Wiley, Newyork.
11. Howard C V and Reed M. 1998. Unbiased stereology, ISBN-387-91516-8, Springer.
12. Pattan Prakash, Mytri V. D. and Hiremath P. S. 2009. Automatic Microstructure Image Analysis for Classification and Quantification of Phases of Material, in Proc. of Intl. Conf. on Systemics, Cybernetics and Informatics (ICSCI) Hyderabad, pp. 308-311.
13. Neil Brent and Russ J. C. 2012. Measuring shapes, CRC Press, Newyork.
14. Exner H. E. 2004. Stereology and 3D Microscopy Useful alternatives or competitors in the quantitative Analysis of microstructures? Image Anal Stereol, vol 23, pp. 73-82.
15. Microstructures Libraries: http://www. metalograf. de/start-eng. htm and www. doitpoms. ac. uk.
16. P. S. Hiremath and Anita Sadashivappa. 2013. Selective Median Switching Filter for Noise Suppression in Microstructure Images of Material(SMSF), Intl' J. of Image Processing, Vol. 7:1, pp. 101-108.
17. Pattan Prakash, Mytri V. D. and Hiremath P. S. 2011. Digital Microstructure Analysis System for Testing and Quantifying the Ductile Cast Iron, Intl' J. of Computer Applications (IJCA), Vol. 19, No. 3, pp. 22-27.

Stereology Otsu's Segmentation Cast Iron Microstructure.