A new method to quantify parameters of membrane morphology from electron microscopy micrographs by texture recognition

Abstract A new method has been developed in order to automatically quantify parameters of membrane morphology from micrographs obtained through microscopy techniques. The parameters estimated by this algorithm are: pore size distribution, porosity, pore symmetry, regularity and tortuosity, as well as various statistical measures. These properties determine the performance of a membrane. The proposed method is based on texture recognition. It first identifies the pores present in the membrane from a cross-section micrograph of it, then labels them and finally makes the corresponding measurements. The main difference and advantage of this technique with respect to previous proposals is that the algorithm does not perform generic particle recognition, but direct scanning of typical pore structures and no user decisions are needed in all the steps of the process. Additionally, the proposed technique does not only determine typical parameters, such as pore size, but also particular characteristics of membrane topology, such as symmetry. The source information consists of cross-section membrane micrographs that can be typically obtained from electron microscopy (scanning or transmission), as well as from other types of microscopy, which are the most common acquisition techniques used by membranologists. The system provides quantitative, systematic and fast results, which represents a significant advance in the field of membrane analysis.

[1]  Debasis Sarkar,et al.  In situ particle size estimation for crystallization processes by multivariate image analysis , 2009 .

[2]  W. Kools.  Membrane formation by phase inversion in multicomponent polymer systems. Mechanisms and morphologies , 1998 .

[3]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[6]  Marco Mazzotti,et al.  Measurement of size and shape distributions of particles through image analysis , 2008 .

[7]  X. Thibault,et al.  Morphological characterization of a polymeric microfiltration membrane by synchrotron radiation computed microtomography , 2007 .

[8]  R. Baker,et al.  Preparation of asymmetric loeb‐sourirajan membranes , 1973 .

[9]  Anil K. Jain,et al.  Address block location on envelopes using Gabor filters , 1992, Pattern Recognit..

[10]  Jan D. Miller,et al.  Pore structure and network analysis of filter cake , 2000 .

[11]  B. S. Manjunath,et al.  A texture thesaurus for browsing large aerial photographs , 1998 .

[12]  Gilles Bertrand,et al.  Watersheds, mosaics, and the emergence paradigm , 2005, Discret. Appl. Math..

[13]  Laura Palacio Martínez.  Caracterización estructural y superficial de membranas microporosas , 2011 .

[14]  Marcel Mulder,et al.  Basic Principles of Membrane Technology , 1991 .

[15]  Michael A King,et al.  Particle detection, number estimation, and feature measurement in gene transfer studies: optical fractionator stereology integrated with digital image processing and analysis. , 2002, Methods.

[16]  Edward R. Dougherty,et al.  Mathematical Morphology in Image Processing , 1992 .

[17]  Kevin J. Roberts,et al.  Multi-scale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers , 2005 .

[18]  M. Pons,et al.  Quantification, by image analysis, of effect of operational conditions on size and shape of precipitated barium sulphate , 2002 .

[19]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[20]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[21]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[22]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  B. S. Manjunath,et al.  EdgeFlow: a technique for boundary detection and image segmentation , 2000, IEEE Trans. Image Process..

[24]  Ricard Garcia-Valls,et al.  Quantification of membrane morphology by interpretation of scanning electron microscopy images , 2004 .

[25]  R. Ziel,et al.  Quantification of the pore size distribution (porosity profiles) in microfiltration membranes by SEM, TEM and computer image analysis , 2008 .

[26]  Domènec Puig,et al.  Supervised texture classification by integration of multiple texture methods and evaluation windows , 2007, Image Vis. Comput..

[27]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Proceedings of International Conference on Image Processing.