Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis

详细信息	查看全文 | 推荐本文 |

• 作者：Nicolas Lomé ; nie^a ; ^b ; ^{lomenie@mi.parisdescartes.fr}  ; [Author Vitae] ; Daniel Racoceanu^a ; ^c  ; [Author Vitae]
• 关键词：Shape analysis ; Mesh analysis ; Unorganized point set ; Spatial relation modeling ; Mathematical morphological operator ; Image exploration ; Graph representation ; Semantic query ; Visual reasoning ; Digital histopathology
• 刊名：Pattern Recognition
• 出版年：2012
• 期刊代码：100_00313203
• 类别：cp
• 出版时间：August, 2012
• 卷：45
• 期：8
• 页码：2894-2911
• 文件大小：4979 K

摘要

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 鈥渂etween鈥?or 鈥渓eft of鈥?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.