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A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing
Author(s) -
Serin Faruk,
Erturkler Metin
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22372
Subject(s) - delaunay triangulation , voronoi diagram , graph , computer science , bowyer–watson algorithm , geometric graph theory , pattern recognition (psychology) , artificial intelligence , mathematics , combinatorics , algorithm , line graph , geometry , graph power
The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell‐location‐based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance‐based threshold, K‐Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell‐graph. Histopathological images of liver were classified by using features extracted from T‐Distance, K‐Nearest Neighbor, Voronoi, Delaunay, and the proposed cell‐graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell‐graph model.