A review of graph-based methods for image analysis in digital histopathology


Digital image analysis of histological datasets is a currently expanding field of research. With different stains, magnifications and types of tissues, histological images are inherently complex in nature and contain a wide variety of visual information. Several image analysis techniques are being explored in this direction. However, graph-based methods are gaining most popularity, as these methods can describe tissue architecture and provide adequate numeric information for subsequent computer-based analysis. Graphs have the ability to represent spatial arrangements and neighborhood relationships of different tissue components, which are essential characteristics observed visually by pathologists during investigation of specimens. In this paper, we present a comprehensive review of the graph-based methods explored so far in digital histopathology. We also discuss the current limitations and suggest future directions in graph-based tissue image analysis.

Diagnostic Pathology


 title = {A review of graph-based methods for image analysis in digital histopathology},
 author = {Sharma, Harshita and Zerbe, Norman and Lohmann, Sebastian and Kayser, Klaus and Hellwich, Olaf and Hufnagl, Peter},
 copyright = {Copyright (c) 2015 Harshita Sharma, Norman Zerbe, Sebastian Lohmann, Klaus Kayser, Olaf Hellwich, Peter Hufnagl},
 doi = {10.17629/www.diagnosticpathology.eu-2015-1:61},
 issn = {ISSN 2364-4893},
 journal = {Diagnostic Pathology},
 language = {en},
 number = {1},
 url = {http://www.diagnosticpathology.eu/ojs-2.4.5/index.php/dpath/article/view/61},
 urldate = {2020-03-04},
 volume = {1},
 year = {2015}