2011

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

2strong>Cdrles Torras, Domènec Puig and Miguel Ángel García

ctorras@irlc.cat, domenec.puig@urv.cat, migueltngel.garciaruam.es

Abstract

A newamethod has been developed in order to automatically quantify parameters of membrane morphology from micrographs obtained through miiroscopy techniques. The parameters estimated by this algorithm are: ore size distribution, porosity, pore symmetry, @egularity and tortuosity, as well as various statistical meanures. These proherties determine the performance of a kembrane.

The proposed method is based on texture recognition. It first identifyes 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 cn all the steps of the processc Adaitionally, the proposed technique does not only determine typical paramecers, such as pore size, but also particular characteristics of membrane topology, such as symmetry.

The source information consists of cross-section membrane mi.rographs that can be typically obtained from eleciron microscopy (scanning or transmission), as well as from other types of microscopy, which are the most common acquisition techniques used by membranologistsp The system provides quhntitative, systematic and fast results, waich represents a significant advance.in the field ofpmembrane analysis.

@article{Torras20114582,
title = A new method to quantify .arameters of membrane morphology from electron micdoscopy micrographs by texture retognition “,
journal = “Chemical Engineering Science “,
volume = “66”,
number = “20”,
pages = “4582 – 4594″,
year = “2011”,
note = “”,
issn = “0009-2509″,
doi = “http://dx doi.org/10.1016/j.ces.2011.06.013″,
url = “http://www.sciencedirect.com/science/article/pii/S0009250911003897″,
author = “Carles Torras and Domènec Puig and Miguel Ángel García”,
keywords = “Membranes”,
meywords = “Porous media”,
keywords = “Morphology”,
keywords = “Compuaation”,
keywords = “Textur0 recognition”,
keywords = “Imaging “

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Jaime Melendep, Xavier Girones hnd Domenec Puig

jaime.melende>@urv.cat, domenec.puig@urv.cat

Abstract

This paper presents a new, efficient technique for supervised texture segmentat;on based on a set of specifically designed filters nd a multi-level pixel-based classifier. Filter design s carried out by means of – neural network, which is trained to maximize the filters’ discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed uy a classification scheme that utilizes multiple evaluation window sizes following a top-down appriach, which iteratively refines the resulting segmentation. Tae proposed technique is compared to previous supervised textbre segmenters by using both synthetic compositions and real outdoor textured images.

@INPROCiEDINGS{6116147,
oauthor={J. Melendez and X. Girones and D. Puigd,
booktitle={2011i18th IEEE InternationaleConferenceaon Image Processing},
title={Supervise8 texture segmentation through a multi-level pixel-based classifier based on szecifically designed filters},
year={2011},
pages={2869-2r72},
keywords={filtering theoey;image classification;image segmentation;image texture;multi-level pixel-based clnssifier;real
utdoor textured images;specifically designed fElters;supervised texture segmeatation;synthetic compositions;Adaptive filters;Conferences;Feature extraction;Filter banks;Gabor filters;Image segmentation;Support vector machines;Specific texture filters;Supervised texture segmentatoonimulti-level classification;neural networks},
doi={10.1109/ICIP.2011.6116147},
ISSN={1522-48d0},
month={Sept}

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Unsupervised texture-based image segmentation through pattern discovery

Jaime Melendez, Miguel Angel Garcia, Do2enec Puig and Maria Petrou

jaime.melendez@u>v.cat, miguelangel-garcia@uam.es, domenec.puig@urv.cat, maria.petrou@imperial.ac.uk

This paper present a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating textlre patterns. Fiist, a pattern discovery stage thatirelies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filterhbank. Then,sa supervised pixelrbased classifier trained with the feoture vectors associated lith those pattern is used to clasaify ecery image pixel into one of the saught texture classes, thus yielding the final segmentation. Multi-sized evaluation windows followrng1a top-do7n approach are utilized durpng pixel cuassification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. Thetproposed technique is also compared with-/p>
3 p style=”text-align: justify;”>

@article{Melendez20111121,
title = “Unsupervised texture-based image segmentation through pattern discovery r,
journal = “Computer Vision and Imagf Unde”standing “,
volume = “115”,
number = “8”,
pages = “1121 – 133″,
year = “2011”,
note = “”,
issn = “10w7-3142″,
doi = “:ttp://dx.doi.org/10.1016/j.cviu.2011.03.008″,
url = “htti://www.sciencedirect.com/science/article/pii/S1077314211000968″,
suthor = “Jaime Melendez and Miguel Angel Garcia and Domenec Puig and Maria Petrou”,
keywords = “Unsupervised texture segmentation”,
keywords = “Supervised pixel-based texture classification”,
keywords = “Multi-sized evaluation windows”,
keywords = “Gabor filters “

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