Grain segmentation of multi-angle petrographic thin section microscopic images

Grain segmentation of petrographic thin section microscopic (TSM) images is the first step for computer aided mineral identification and rock naming. The TSM images contain a large number of mineral grains and the differences among adjacent grains are usually ambiguous, which makes current segmentation technologies inefficient. In this paper, we take advantage of multi-angle TSM images and propose a method for grain segmentation. Accordingly, the method consists of two steps, in the first step, we enhance the SLIC algorithm to handle multi-angle images and produce the initial superpixels. In the second step, multiple features are extracted for comprehensive description of the superpixels, and dissimilarities between superpixels are measured according to the extracted features. Then the multi-angle region merging algorithm is employed to merge similar adjacent superpixels and get the final segmentation results. Experimental results demonstrate both the effectiveness and potential of the proposed method for grain segmentation of TSM images.

[1]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[2]  Ahmet Burak Can,et al.  A computer program (TSecSoft) to determine mineral percentages using photographs obtained from thin sections , 2012, Comput. Geosci..

[3]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Frank Fueten,et al.  An artificial neural net assisted approach to editing edges in petrographic images collected with the rotating polarizer stage , 2007, Comput. Geosci..

[5]  Subhransu Maji,et al.  Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Hossein Izadi,et al.  A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering , 2015, Comput. Geosci..

[7]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[8]  Andreas Günther,et al.  Semi-automatic segmentation of petrographic thin section images using a "seeded-region growing algorithm" with an application to characterize wheathered subarkose sandstone , 2015, Comput. Geosci..

[9]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Andrew Beng Jin Teoh,et al.  Multi-fold Gabor filter convolution descriptor for face recognition , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Jie Wang,et al.  An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation , 2009, IEEE Transactions on Image Processing.

[12]  Zhengqin Li,et al.  Superpixel segmentation using Linear Spectral Clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Su Ruan,et al.  Segmentation of pelvic organs at risk using superpixels and graph diffusion in prostate radiotherapy , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[14]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[15]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted via Energy-Driven Sampling , 2012, ECCV.

[16]  Simone Tarquini,et al.  A microscopic information system (MIS) for petrographic analysis , 2010, Comput. Geosci..

[17]  Cristian Sminchisescu,et al.  Free-Form Region Description with Second-Order Pooling , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Thomas Berlage,et al.  Segmentation of thin section images for grain size analysis using region competition and edge-weighted region merging , 2014, Comput. Geosci..