Segmentation of sandstone thin section images with separation of touching grains using optimum path forest operators

Autor(es)

Mingireanov Filho, Ivan ^[1] ; Spina, Thiago Vallin ^[2] ; Falcao, Alexandre Xavier ^[3] ; Vidal, Alexandre Campane ^[4]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Petr Sci & Engn, Fac Mech Engn, Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Lab Visual Informat, Inst Comp, Campinas, SP - Brazil

^[3] Univ Estadual Campinas, Dept Informat Syst, Inst Comp, Campinas, SP - Brazil

^[4] Univ Estadual Campinas, Dept Geol & Nat Resources, Inst Geosci, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Computers & Geosciences; v. 57, p. 146-157, AUG 2013.
• Citações Web of Science: 5

Resumo

The segmentation of detrical sedimentary rock images is still a challenge for characterization of grain morphology in sedimentary petrography. We propose a fast and effective approach that first segments the grains from pore in sandstone thin section images and separates the touching grains automatically, and second lets the user to correct the misclassified grains with minimum interaction. The method is mostly based on the image foresting transform (IFT)-a tool for the design of image processing operators using optimum connectivity. The IFT interprets an image as a graph, whose nodes are the image pixels, the arcs are defined by an adjacency relation between pixels, and the paths are valued by a connectivity function. The IFT algorithm transforms the image graph into an optimum-path forest and distinct operators are designed by suitable choice of the IFT parameters and post-processing of the attributes of that forest. The solution involves a sequence of three IFT-based image operators for automatic segmentation and the interactive segmentation combines region- and boundary-based object delineation using two IFT operators. Tests with thin section images of two different sandstone samples have shown very satisfactory results, yielding r(2) and accuracy parameters of 0.8712 and 94.8% on average, respectively. Biases were the presence of the matrix and rock fragments. (C) 2013 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 11/01434-9 - Segmentação interativa de objetos em vídeo digital usando grafos e modelos nebulosos de conhecimento de conteúdo
• Beneficiário: Thiago Vallin Spina
• Linha de fomento: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 07/52015-0 - Métodos de aproximação para computação visual
• Beneficiário: Jorge Stolfi
• Linha de fomento: Auxílio à Pesquisa - Temático