Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bagof-Features Image Classification
Stefan G. Stanciu* 1,2 , Shuoyu Xu 3-5 , Qiwen Peng 3,5 , Jie Yan 4,5 , George A. Stanciu 1 , Roy E.
Welsch 3,6 , Peter T.C. So 3,4,7,8 , Gabor Csucs 2 and Hanry Yu 3-5,8-10
1 Center for Microscopy-Microanalysis and Information Processing, University Politehnica of
Bucharest, Romania
2 Light Microscopy and Screening Centre, ETH Zurich, Switzerland
3 Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore
4 Biosystems and Micromechanics IRG, Singapore MIT Alliance for Research and Technology,
Singapore, Singapore
5 Institute of Bioengineering and Nanotechnology, Singapore, Singapore
6 Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
7 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA,
USA
8 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA,
USA
9 Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore,
Singapore, Singapore
10 The Mechanobiology Institute, Singapore, Singapore
*Correspondence to: stefan.stanciu@cmmip-upb.org

Supplementary Information
Supplementary Table 1. Number of DSIFT features extracted from a 1024x1024 pixels image with different grid spacings.
Grid spacing (pixels) 10 20
Number of features 10,404 2,601
40
676
60
289
Supplementary Figure 1. Correspondence between the bin size and the standard deviation of the isotropic Gaussian kernel used for smoothing.

Supplementary Figure 2. Schematic example of the 4-runs strategy used for testing all the images in a sub-set.