Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2012.01.021
Title: Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis
Authors: Loménie, N.
Racoceanu, D. 
Keywords: Digital histopathology
Graph representation
Image exploration
Mathematical morphological operator
Mesh analysis
Semantic query
Shape analysis
Spatial relation modeling
Unorganized point set
Visual reasoning
Issue Date: 2012
Source: Loménie, N., Racoceanu, D. (2012). Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis. Pattern Recognition 45 (8) : 2894-2911. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2012.01.021
Abstract: High-level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields. In particular, it is deemed important whenever one needs to mine high-content images or large scale image databases in a more expressive way than a purely statistically one. Continuing previous efforts to incorporate structural analysis by developing specific efficient morphological tools performing on mesh representations like Delaunay triangulations, we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets. We provide an original mesh lattice framework which is more convenient for structural representations of large image data by means of interest point sets and their morphological analysis. The set of designed numerical operators is based on a specific dilation operator that makes it possible to handle concepts like between or left of over sparse representations of image data such as graphs. Based on this new theoretical framework for reasoning about images, we are able to process high-level queries over large histopathological images, knowing that digitized histopathology is a new challenge in the field of bio-imaging due to the high-content nature and large size of these images. © 2012 Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/42326
ISSN: 00313203
DOI: 10.1016/j.patcog.2012.01.021
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