Title
Structural Analysis of Histological Images to Aid Diagnosis of Cervical Cancer
Abstract
The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. The overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological images. For this pourpose, a pipeline of morphological operators were implemented for the segmentation of cell nuclei and the Delaunay Triangulation were used in order to represent the tissue architecture. Also, clustering algorithms and graph morphology were used to automatically obtain the boundary between the histological layers of the epithelial tissue. Similarity criteria and adjacency relations between the triangles of the network were explored. The proposed method was evaluated concerning the detection of the presence of lesions in the tissue as well as the their malignancy grading.
Year
DOI
Venue
2012
10.1109/SIBGRAPI.2012.51
Graphics, Patterns and Images
Keywords
Field
DocType
biological tissues,cancer,cellular biophysics,graph theory,image representation,image segmentation,medical image processing,pattern clustering,CIN,Delaunay triangulation,adjacency relation,cell nuclei segmentation,cervical cancer diagnosis,cervical intraepithelial neoplasia,clustering algorithm,computer-aided diagnosis,epithelial tissue,graph morphology,histological images,histological layers,histopathological images,lesion detection,malignancy grading,medical image processing,similarity criteria,structural analysis,tissue architecture representation,Cervical Intraepithelial Neoplasia (CIN),Computer-Aided Diagnosis,Medical Image Processing,Neighborhood Graphs
Adjacency list,Cervical cancer,Computer vision,Segmentation,Computer science,Cervical intraepithelial neoplasia,Computer-aided diagnosis,Image segmentation,Artificial intelligence,Cluster analysis,Delaunay triangulation
Conference
ISSN
ISBN
Citations 
1530-1834
978-1-4673-2802-9
3
PageRank 
References 
Authors
0.38
1
4